market efficiency hypothesis

13CHAPTER

Stock market efficiency

LEARNING OUTCOMES

By the end of this chapter the reader should be able to:

� discuss the meaning of the random walk hypothesis and provide a balanced judgement of the usefulness of past price movements to predict future share prices (weak-form efficiency);

� provide an overview of the evidence for the stock market’s ability to take account of all publicly available information including past price movements (semi-strong efficiency);

� state whether stock markets appear to absorb all relevant (public or private) information (strong-form efficiency);

� outline some of the behavioural-based arguments leading to a belief in inefficiencies;

� comment on the implications of the evidence for efficiency for investors and corporate management.

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The question of whether the stock market is efficient in pricing shares and other securities has fasci-nated academics, investors and businessmen for a long time. This is hardly surprising: even academics are attracted by the thought that by studying in this area they might be able to discover a stock market inefficiency which is sufficiently exploitable to make them very rich, or at least, to make their name in the academic community. In an efficient market systematic undervaluing or overvaluing of shares does not occur, and therefore it is not possible to develop trading rules which will ‘beat the market’ by, say, buying identifiable underpriced shares, except by chance. However, if the market is inefficient it regularly prices shares incorrectly, allowing a perceptive investor to identify profitable trading oppor-tunities.1 This is an area of research where millions have been spent trying to find ‘nuggets of gold’ in the price movements of securities. A small amount of this money has been allocated to university departments, with the vast majority being spent by major securities houses around the world and by people buying investment advice from professional analysts offering to ‘pick winners’. Money has also been taken from the computer literati paying for real-time stock market prices and analytical software to be piped into their personal computer, and by the millions of buyers of books which promise riches beyond imagining if the reader follows a few simple stock market trading rules. They do say that a fool and his money are soon parted – never was this so true as in the world of stock market investment with its fringe of charlatans selling investment potions to cure all financial worries. This chapter may help the reader to discern what investment advice is, and is not, worth paying for. But this is too limited an ambition; the reader should also appreciate the significance of the discovery that for most of the people and for most of the time the stock market in an unbiased way prices shares given the information available (and it is extremely difficult to make more than normal returns). There are profound implications for business leaders and their interaction with the share mar-kets, for professional fund managers, and for small investors.

What is meant by efficiency?

In an efficient capital market, security (for example shares) prices rationally reflect available information.

The efficient market hypothesis (EMH) implies that, if new information is revealed about a firm, it will be incorporated into the share price rapidly and rationally, with respect to the direc-tion of the share price movement and the size of that movement. In an efficient market no trader will be presented with an opportunity for making a return on a share (or other security) that is greater than a fair return for the risk associated with that share, except by chance. The absence of abnormal profit possibilities arises because current and past information is immediately reflected in current prices. It is only new information that causes prices to change. News is by definition unforecastable and therefore future price changes are unforecastable. Stock market efficiency does not mean that investors have perfect powers of prediction; all it means is that the current share price level is an unbiased estimate of its true economic value based on the information revealed. Market efficiency does not mean that share prices are equal to true value at every point in time. It means that the errors that are made in pricing shares are unbiased; price deviations from true value are random. Fifty per cent of efficiently priced shares turn out to perform better than the market as a whole and 50 per cent perform worse; the efficient price is unbiased in the statisti-cal sense. So if Marks & Spencer’s shares are currently priced at £7 it could be, over the next five

Introduction

Chapter 13 • Stock market efficiency 543

1 Even though this discussion of the efficient markets hypothesis is set within the context of the equity mar-kets in this chapter, it must be noted that the efficient pricing of financial and real assets is discussed in many contexts; from whether currencies are efficiently priced vis-à-vis each other to the pricing of com-modities, bonds, property and derivative instruments.

years, that we discover they were grossly overpriced at £7, or that events show them to be under-priced at £7. Efficiency merely means that there is an equal chance of our being too pessimistic at £7 as being too optimistic. The same logic applies to shares on high or low price-earnings ratios (PERs). That is, shares with low PERs should be no more likely to be overvalued or underval-ued than shares with high PERs. Both groups have an equal chance of being wrongly priced given future economic events on both the upside and the downside. In the major stock markets of the world prices are set by the forces of supply and demand. There are hundreds of analysts and thousands of traders, each receiving new information on a company through electronic and paper media. This may, for example, concern a technological breakthrough, a marketing success or a labour dispute. The individuals who follow the market are interested in making money and it seems reasonable to suppose that they will try to exploit quickly any potentially profitable opportunity. In an efficient market the moment an unexpected, positive piece of information leaks out investors will act and prices will rise rapidly to a level which gives no opportunity to make further profit. Imagine that BMW announces to the market that it has a prototype electric car which will cost £10,000, has the performance of a petrol-driven car and will run for 500 miles before needing a low-cost recharge. This is something motorists and environmentalists have been demanding for many years. The profit-motivated investor will try to assess the value of a share in BMW to see if it is currently underpriced given the new information. The probability that BMW will be able suc-cessfully to turn a prototype into a mass market production model will come into the equation. Also the potential reaction of competitors, the state of overall car market demand and a host of other factors have to be weighed up to judge the potential of the electric car and the future returns on a BMW share. No analyst or shareholder is able to anticipate perfectly the commercial viability of BMW’s technological breakthrough but they are required to think in terms of probabilities and attempt to make a judgement. If one assumes that the announcement is made on Monday at 10 a.m. and the overwhelming weight of investor opinion is that the electric car will greatly improve BMW’s share returns, in an efficient market the share price will move to a higher level within seconds. The new higher price at 10.01 a.m. is efficient but incorporates a different set of information to that incorporated in the price prevailing at 10 a.m. Investors should not be able to buy BMW shares at 10.01 a.m. and make abnormal profits except by chance.

Most investors are too lateEfficiency requires that new information is rapidly assimilated into share prices. In the sophis-ticated financial markets of today the speedy dissemination of data and information by cheap electronic communication means that there are large numbers of informed investors and advis-ers. These individuals are often highly intelligent and capable of fast analysis and quick action, and therefore there is reason to believe many stock markets are efficient at pricing securities. However, this belief is far from universal. Thousands of highly paid analysts and advisers main-tain that they can analyse better and act more quickly than the rest of the pack and so make abnormally high returns for their clients. There is a well-known story which is used to mock the efficient market theoreticians: A lecturer was walking along a busy corridor with a student on his way to lecture on the effi-cient market hypothesis. The student noticed a £20 note lying on the floor and stooped to pick it up. The lecturer stopped him, saying, ‘If it was really there, someone would have picked it up by now’. With such reasoning the arch-advocates of the EMH dismiss any trading system which an investor may believe he has discovered to pick winning shares. If this system truly worked, they say, someone would have exploited it before and the price would have already moved to its effi-cient level. This position is opposed by professional analysts: giving investment advice and managing col-lective funds is a multi-billion pound industry and those employed in it do not like being told that most of them do not beat the market. However, a few stock pickers do seem to perform extraordi-narily well on a consistent basis over a long period of time. There is strong anecdotal evidence that some people are able to exploit inefficiencies – we will examine some performance records later.

Part 4 • Sources of finance 544

What efficiency does not meanTo provide more clarity on what efficiency is, we need to deal with a few misunderstandings held by people with a little knowledge (a dangerous thing):

� Efficiency means that prices do not depart from true economic value This is false. At any one time we would expect most shares to deviate from true value, largely because value depends on the future, which is very uncertain (see Chapter 17 on share valuation). However, under the EMH we would expect the deviations to be random.

� You will not come across an investor beating the market in any single time period This is false because you would expect, in an efficient market, that approximately one-half of shares bought subsequently outperform. So, many investors, unless they buy such a broad range of shares that their portfolio tracks the market, would outperform. Note that, under the EMH, this is not due to skill, but simply caused by the randomness of price deviations from true eco-nomic value.

� No investor following a particular investment strategy will beat the market in the long term This is false simply because there are millions of investors. In a completely efficient market, with prices deviating in a random fashion from true value, it is likely that you could find a few investors who have outperformed the market over many years. This can happen because of the laws of probability; even if the probability of your investment approach beating the market is very small, the fact that there are millions of investors means that, purely by chance, a few will beat the market. Unfortunately, it is very difficult to investigate whether a long-term outper-formance is luck or evidence against the EMH. We look at the performance of someone who has consistently outperformed for more than 60 years, Warren Buffett, later in the chapter. Some people believe his success is due to luck in an efficient market, others put it down to superior share-picking ability – you will have to make up your own mind.

Types of efficiencyEfficiency is an ambiguous word and we need to establish some clarity before we go on. There are three types of efficiency:

1 Operational efficiency This refers to the cost, speed and reliability of transactions in securi-ties on the exchange. It is desirable that the market carries out its operations at as low a cost as possible, speedily and reliably. This may be promoted by creating as much competition between market makers and brokers as possible so that they earn only normal profits and not excessively high profits. It may also be enhanced by competition between exchanges for sec-ondary-market transactions.

2 Allocational efficiency Society has a scarcity of resources (that is, they are not infinite) and it is important that we find mechanisms which allocate those resources to where they can be most productive. Those industrial and commercial firms with the greatest potential to use investment funds effectively need a method to channel funds their way. Stock markets help in the process of allocating society’s resources between competing real investments. For exam-ple, an efficient market provides vast funds for the growth of the electronics, pharmaceuticals and biotechnology industries (through new issues, rights issues, etc.) but allocates only small amounts for slow-growth industries.

3 Pricing efficiency It is pricing efficiency that is the focus of this chapter, and the term ‘efficient market hypothesis’ applies to this form of efficiency only. In a pricing-efficient market the investor can expect to earn merely a risk-adjusted return from an investment as prices move instantaneously and in an unbiased manner to any news.

The black line in Exhibit 13.1 shows an efficient market response to BMW’s (fictional) announce-ment of an electric car. The share price instantaneously adjusts to the new level. However, there are four other possibilities if we relax the efficiency assumption. First, the market could take a long time to absorb this information (under-reaction) and it could be only after the tenth day that the share price approaches the new efficient level. This is shown in Line 1. Secondly, the market

545Chapter 13 • Stock market efficiency

could anticipate the news announcement – perhaps there have been leaks to the press, or senior BMW management has been dropping hints to analysts for the past two weeks. In this case the share price starts to rise before the announcement (Line 2). It is only the unexpected element of the announcement that causes the price to rise further on the announcement day (from point A to point B). A third possibility is that the market overreacts to the new information (Line 3); the ‘bubble’ deflates over the next few days. Finally, the market may fail to get the pricing right at all and the shares may continue to be underpriced for a considerable period (Line 4).

The value of an efficient marketIt is important that share markets are efficient for at least three reasons.

1 To encourage share buying Accurate pricing is required if individuals are going to be encour-aged to invest in private enterprise. If shares are incorrectly priced many savers will refuse to invest because of a fear that when they come to sell the price may be perverse and may not represent the fundamental attractions of the firm. This will seriously reduce the availability of funds to companies and inhibit growth. Investors need to know they are paying a fair price and that they will be able to sell at a fair price – that the market is a ‘fair game’.

2 To give correct signals to company managers In Chapter 1 it was stated, for the purposes of this book, that the objective of the firm was the maximisation of shareholder wealth. This can be represented by the share price in an efficient market. Sound financial decision making therefore relies on the correct pricing of the company’s shares. In implementing a share-holder wealth-enhancing decision the manager will need to be assured that the implication of the decision is accurately signalled to shareholders and to management through a rise in the share price. It is important that managers receive feedback on their decisions from the share market so that they are encouraged to pursue shareholder wealth strategies. If the share market continually gets the pricing wrong, even the most shareholder-orientated manager will find it difficult to know just what is required to raise the wealth of the owners.

In addition share prices signal the rate of return investors demand on securities of a par-ticular risk class. If the market is inefficient the risk–return relationship will be unreliable.


Line 1 Slow reaction

Line 4 Persistent inefficiency

Efficient market

Line 3 Overreactionfollowed by deflation

Line 2 Anticipatoryprice movements(information leak)

AB

BMW

sha

re p

rice

–10 –5 0Announcement

date

+5 +10 Days before (–)and days after (+)announcement

Exhibit 13.1 New information (an electric car announcement by BMW) and alternative stock market reactions – efficient and inefficient

Managers need to know the rate of return they are expected to obtain on projects they under-take. If shares are wrongly priced there is a likelihood that in some cases projects will be wrongly rejected because an excessively high cost of capital (discount rate) is used in project appraisal. In other circumstances, if the share prices are higher than they should be the cost of capital signalled will be lower than it should be and projects will be accepted when they should have been rejected.

Correct pricing is not just a function of the quality of the analysis and speed of reaction of the investment community. There is also an onus placed on managers to disclose information. Shares can only be priced efficiently if all relevant information has been communicated to the market. Managers neglect this issue at their peril.

3 To help allocate resources Allocational efficiency requires both operating efficiency and pricing efficiency. If a poorly run company in a declining industry has highly valued shares because the stock market is not pricing correctly then this firm will be able to issue new shares, and thus attract more of society’s savings for use within its business. This would be wrong for society as the funds would be better used elsewhere.

Random walks

Until the early 1950s it was generally believed that investment analysis could be used to beat the market. In 1953 Maurice Kendall presented a paper which examined security and commodity price movements over time. He was looking for regular price cycles, but was unable to identify any. The prices of shares, etc. moved in a random fashion – one day’s price change cannot be pre-dicted by looking at the previous day’s price change. There are no patterns or trends. An analogy has been drawn between security and commodity price changes and the wanderings of a drunken man placed in the middle of a field. Both follow a random walk, or to put it more technically, there is no systematic correlation between one movement and subsequent ones. To many people this is just unacceptable. They look at a price chart of a share and see pat-terns; they may see an upward trend running for months or years, or a share price trapped between upper and lower resistance lines. They also point out that sometimes you get persistent movements in shares; for example a share price continues to rise for many days. The statisticians patiently reply that the same apparent pattern or trends can occur purely by chance. Readers can test this for themselves: try tossing a coin several times and recording the result. You will probably discover that there will be periods when you get a string of heads in a row. The apparent patterns in stock market prices are said to be no more significant for predicting the next price movement than the pattern of heads or tails is for predicting what the next toss will produce. That is, they both follow a random walk. To reinforce this look at Exhibit 13.2, which shows two sets of price movements. Many char-tists (those who believe future prices can be predicted from past changes) would examine these and say that both display distinct patterns which may enable predictions of future price move-ments. One of the charts follows the FTSE 100 index each week over a two-year period. The other was generated by the writer’s six-year-old son. He was given a coin and asked to toss it 110 times. Starting at a value of 100, if the first toss was a head the ‘weekly return’ was 4 per cent, if a tail it was –3 per cent. Therefore the ‘index’ for this imaginary share portfolio has a 50 : 50 chance of ending the first week at either 104 or 97. These rules were applied for each of the imaginary 110 weeks. This chart has a positive drift of 1 per cent per week to imitate the tendency for share indi-ces to rise over time. However, the price movements within that upward drift are random because successive movements are independent. Dozens of researchers have tested security price data for dependence. They generally calculate correlation coefficients for consecutive share price changes or relationships between share prices at intervals. The results show a serial correlation of very close to zero – sufficiently close to prevent reliable and profitable forecasts being made from past movements.


Why does the random walk occur?A random walk occurs because the share price at any one time reflects all available information and it will only change if new information arises. Successive price changes will be independent and prices follow a random walk because the next piece of news (by definition) will be inde-pendent of the last piece of news. Shareholders are never sure whether the next item of relevant information is going to be good or bad – as with the heads and tails on a coin there is no relation-ship between one outcome and the next. Also, there are so many informed market traders that as soon as news is released the share price moves to its new rational and unbiased level. We can see how an efficient market will not permit abnormal profits by examining Exhibit 13.3. Here a chartist at time A has identified a cyclical pattern. The chartist expects that over the next six months the share price will rise along the dotted line and is therefore a ‘buy’. However, this chartist is not the only participant in the market and as soon as a pattern is observed it dis-appears. This happens because investors rush to exploit this marvellous profit opportunity. As a result of the extraordinary buying pressure the price immediately rises to a level which gives only the normal rate of return. The moment a pattern becomes discernible in the market it disappears under the weight of buy or sell orders.


80

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(b) Weeks

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Exhibit 13.2 Charts showing the movements on the FT 100 share index and a randomly generated index of prices. Which is which?

The three levels of efficiency

Economists have defined different levels of efficiency according to the type of information which is reflected in prices. Fama (1970) produced a three-level grading system to define the extent to which markets were efficient.2 These were based on different types of investment approaches which were supposedly designed to produce abnormal returns.

1 Weak-form efficiency Share prices fully reflect all information contained in past price move-ments. It is pointless basing trading rules on share price history as the future cannot be predicted in this way.

2 Semi-strong form efficiency Share prices fully reflect all the relevant publicly available information. This includes not only past price movements but also earnings and dividend announcements, rights issues, technological breakthroughs, resignations of directors, and so on. The semi-strong form of efficiency implies that there is no advantage in analysing publicly available information after it has been released, because the market has already absorbed it into the price.

3 Strong-form efficiency All relevant information, including that which is privately held, is reflected in the share price. Here the focus is on insider dealing, in which a few privileged individuals (for example directors) are able to trade in shares, as they know more than the normal investor in the market. In a strong-form efficient market even insiders are unable to make abnormal profits – as we shall see the market is acknowledged as being inefficient at this level of definition.

Weak-form tests

If weak-form efficiency is true a naive purchase of a large, broadly based portfolio of shares typi-cally produces returns the same as those purchased by a ‘technical analyst’ poring over historical share price data and selecting shares on the basis of trading patterns and trends. There will be no mechanical trading rules based on past movements which will generate profits in excess of the average market return (except by chance). Consider some of the following techniques used by technical analysts (or chartists) to identify patterns in share prices.

2 Fama (1991) slightly changed the definitions later but the original versions have the virtues of elegance and simplicity.


A B6 monthsTime

Shar

e pr

ice

Movementexpected by chartist

Actual movement after ‘pattern’ is identified

Exhibit 13.3 A share price pattern disappears as investors recognise its existence

A simple price chartA true chartist is not interested in estimating the intrinsic value of shares. A chartist believes that a chart of the price (and/or volume of trading data) is all that is needed to forecast future price move-ments. Fundamental information, such as the profit figures or macroeconomic conditions, is merely a distraction from analysing the message in the chart. One of the early chartists, John Magee, was so extreme in trying to exclude any other influences on his ‘buy’ or ‘sell’ recommendations that he worked in an office boarded up so that he was not aware of the weather. Exhibit 13.4 shows one of the best known patterns to which chartists respond – it is called a head and shoulders formation.

A head and shoulders pattern like the one shown in Exhibit 13.4 is supposed to herald the start of a major price drop. The left shoulder is formed, according to the chartists, by some investors taking profits after a large price rise, causing a minor price drop. The small fall encourages new buyers, hoping for a continuation of the price rally. They keep pushing the shares above the pre-vious high, but prices soon drift down again, often to virtually the same level at which the left shoulder’s decline ended. It drops to a support level called the neckline. Finally the right shoulder is formed by another wave of buying (on low volume). This peters out, and when the prices fall below the neckline by, say, 3 per cent, it is time to sell. Some chartists even go so far as to say that they can predict the extent of the fall below the neckline – this is in proportion to the distance AB. Exhibit 13.5 provides another chart with a pattern, where the share price trades between two trend lines until it achieves ‘breakout’ through the ‘resistance line’. This is a powerful ‘bull signal’ – that is, the price is expected to rise significantly thereafter.

A

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Exhibit 13.4 The ‘head and shoulders’ pattern


Breakout

Resistance line

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Exhibit 13.5 A ‘line and breakout’ pattern

Chartists have a very serious problem in that it is often difficult to see a new trend until after it has happened. Many critical voices say that it is impossible for the chartist to act quickly enough on a buy or sell signal because competition among chartists immediately pushes the price to its efficient level. To overcome this, some traders start to anticipate the signal, and buy or sell before a clear breakthrough is established. This leads other traders to act even earlier, to lock themselves into a trade before competition causes a price movement. This, it is argued by EMH proponents, will lead to trends being traded away and prices adjusting to take into account all information regarding past price movements, leading us back to the weak form of stock market efficiency. In academic studies modern high-powered computers have been used to simulate chartist trades. Researchers were instructed to find the classic patterns chartists respond to, ranging from ‘triple tops’ and ‘triple bottoms’ to ‘wedges’ and ‘diamonds’.3 The general result was that they found that a simple buy and hold strategy of a broadly based portfolio would have performed just as well as the chartist method, after transaction costs. Dawson and Steeley (2003), for example, found after examining UK share data that ‘economic profits arising from the predictive ability of the technical patterns are unlikely to materialise’. However, some academic studies found evi-dence suggesting trading rules that led to superior returns – see Park and Irwin (2007) for a survey of technical analysis studies.4

The filter approachThe filter technique is designed to focus the trader on the long-term trends and to filter out short-term movements. Under this system a filter level has to be adopted – let us say this is 5 per cent. If the share under observation rises by more than 5 per cent from its low point the trader is advised to buy, as it is in an up-trend. If the share has peaked and has fallen by more than 5 per cent it should be sold. Price movements of less than 5 per cent are ignored. In a down-trend, as well as selling the share the trader owns, the trader should also ‘sell short’, that is, sell shares not yet owned in the anticipation of buying at a later date at a lower price. Again, there has been a consid-erable amount of academic research of various filter rules, and again the general conclusion goes against the claims of the technical analysts – a simple buy and hold policy performs at least as well after transaction costs. Again exceptions to this general conclusion are turning up in the literature – see Park and Irwin (2007).

The Dow theoryCharles Dow, co-founder and editor of the Wall Street Journal, developed, along with others, the Dow theory in the early part of the twentieth century. According to the theory the stock market is characterised by three trends. The primary trend is the most important and refers to the long-term movement in share prices (a year or more). The intermediate trend runs for weeks or months before being reversed by another intermediate trend in the other direction. If an inter-mediate trend is in the opposite direction to the primary trend it is called a secondary reversal (or reaction). These reversals are supposed to retrace between one-third and two-thirds of the primary movement since the last secondary reversal. Tertiary trends, which last for a few days, are less important and need not concern us any further. The left part of Exhibit 13.6 shows a primary up-trend interrupted by a series of intermediate reversals. In the up-trend the reversals always finish above the low point of the previous decline. Thus we get a zigzag pattern with a series of higher peaks and higher lows. The primary up-trend becomes a down-trend (and therefore a sell signal) when an intermedi-ate downward movement falls below the low of the previous reversal (A compared with B) and the next intermediate upward movement does not manage to reach the level of the previous inter-mediate upward spike (C compared with D).

3 For explanations of these terms, the reader is referred to one of the populist ‘how to get rich quickly’ books.

4 Their earlier working paper contains a lot more detail should you wish to pursue this.


In practice there is a great deal of subjectivity in deciding what is, or is not, an intermediate trend. Also primary trends, while relatively easy to identify with hindsight, are extremely difficult to iden-tify at the moment they occur. The verdict of some academic researchers is that a simple buy and hold strategy produces better returns than those produced by the Dow theory, others show more positive results (e.g. Brown et al., 1998).

Moving averagesExamining the history of share prices and applying specific simple trading rules will produce abnormal returns according to Brock et al. (1992). They found that if investors (over the period 1897 to 1986) bought the 30 shares in the Dow Jones Industrial Average when the short-term moving average of the index (the average over, say, 50 days) rises above the long-term moving average (the average over, say, 200 days) they would have outperformed the investor who simply bought and held the market portfolio. Investors would also have achieved abnormal perform-ance if they bought when a share ‘broke out’ from the trading range. ‘However, transaction costs should be carefully considered before such strategies can be implemented’ (Brock et al., 1992). A number of subsequent studies have found good performances (after transaction costs) from fol-lowing rules based on moving average price charts (see Park and Irwin, 2007).

Other strategiesTechnical analysts employ a vast range of trading rules. Some, for example, advise a pur-chase when a share rises in price at the same time as an increase in trading volume occurs. More bizarrely, other investors have told us to examine the length of women’s dresses to get a prediction of stock market moves. Bull markets are apparently associated with short skirts and bear markets (falling) with longer hemlines! Some even look to sunspot activity to help them select shares. A decade or so ago the conclusion from the academic studies on weak-form efficiency was that overwhelmingly the evidence suggested that stock markets correctly incorporated all past price and volume information into current share prices. That is, it is unlikely that you could achieve an extraordinary high return (for the risk level) by identifying patterns in charts, etc. (However, there were many studies that showed profitable technical trading strategies in the commodity and cur-rency markets.) This conclusion now needs to be revised in the light of dozens of recent rigorous academic stud-ies into the profitability of ‘technical analysis’ in shares, as well as further studies into commodities and currency trading. The majority of these indicate that extraordinary high returns are achiev-able (see Park and Irwin, 2007). Note, though, that much of the evidence is disputed. For instance, other academics claim that some of the studies suffer from a number of methodological flaws: data snooping (using the same data to test for a variety of trading strategies and eventually finding one that works in that data set); selecting the trading rule after the period under study; inadequate


TimeM

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Exhibit 13.6 The Dow theory

allowance for extra risk and transaction costs. So this remains a field of intellectual endeavour that is wide open for future enterprising researchers to improve on the research techniques to help us grope towards a conclusion on the profitability of technical trading strategies. Jonathan Davis has become part of a movement to revive technical analysis – see Exhibit 13.7.

553Chapter 13 • Stock market efficiency�

Exhibit 13.7

Three years ago in this space I noted that Dow Theory had given an important technical signal on November 23 2007 indicating that the US equity market had entered ‘a primary down trend’. Although the equity market looked temporarily oversold, what it appeared to mean, if you believed in such things, I suggested, was that ‘investors should be preparing for a market whose underlying trend from here is down, not up’.

Well, that didn’t turn out to be a bad call, as the Dow Jones index subsequently fell by 50 per cent to its March 2009 low, and as of last week was still trading 15 per cent below its level at the time the signal was given. If making market predictions was my only business, as opposed to a sideline, I would by now be trumpeting my amazing track record to anyone who cared to listen.

As it happens, while the technical signals were one of the things that made me cautious on the equity markets in late 2007, in truth my bearish stance was driven more by experience and the negative things I was hearing from professional contacts (including central bankers who admitted privately to having no confidence that they could stop the escalating banking crisis).

Honesty also requires admitting that most of my 800 words were not about the coming market crash, but a discussion of whether technical analysis had any value. My conclusion was that it did best when interpreted by experienced market practitioners with good judgment. ‘Its real value’ I suggested ‘lies in the quality of the interpretation, and that is ultimately subjective, rather than scientific’.

Well, I wouldn’t change a word of that conclusion, but it is fair to say that technical analysis is undergoing a revival from the days when it was routinely dismissed in the academic literature as little more than charlatanism. In his seminal book A Random Walk Down Wall Street, published in 1973, Professor Burton Malkiel dismissed technical analysis with a withering conclusion: ‘under scientific scrutiny, chart-reading must share a pedestal with alchemy’.

Of course, we now know that the random walk, and the efficient market hypothesis to which it is related, were just beginning to dominate the way that academics thought about financial markets. Neither theory seemed to leave any room for technical analysis, which self-evidently was based on the assumption that there was information in security prices that could be exploited by investors either for profit or the avoidance of loss.

The fact that so many investors have continued ever since to rely on price charts to assist decision making suggests the market itself refuses to accept that technical analysis can be so easily refuted. Technical analysis remains the dominant form of analysis in commodity and foreign exchange trading. Sushil Wadhwani, an academic who later moved into investment management, says overcoming the prejudice against technical analysis was the most important lesson he had to learn when moving from the ivory tower into the laboratory of real life experience as a trader.

As a new book by Andrew Lo and Jasmina Hasanhodzic makes clear, the past 20 years have seen the start of a serious re-evaluation, to the point where it is no longer credible to sweep it away as worthless. Prof Lo, one of the brightest stars in the MIT finance faculty, has done as much as anyone to demolish the credibility of the efficient markets hypothesis, and while far from starry-eyed about what technical analysis can rightly now claim to be, concludes that it is ‘a legitimate and useful discipline, tarred by spurious associations and deserving of further academic study’.

A number of recent academic studies have been able to test various trading strategies and found scope for potential profit in them. Other studies have used complex programming to work out from actual market movements trading strategies that would have worked well; often it turns out they correspond closely to seemingly simplistic charting formulae involving moving averages.

The most intriguing finding, though, to my mind, is the evidence the authors present, based on research by Professor Emanuele Viola of Northeastern

Technical analysis pulled out of the binBy Jonathan Davis

Return reversalWe now turn to a group of studies that seem to indicate that the market might consistently fail to price properly. The first area of research concerns the phenomenon of return reversal. That is, shares that have given the highest returns over the previous three to five years (the ‘winners’) generally go on to underperform the stock market over the subsequent three to five years. Those shares that performed worst over a number of years (the ‘losers’) then, on average, show returns significantly higher than the market over the next three to five years. De Bondt and Thaler (1985) selected portfolios of 35 US shares at three-year intervals, between 1933 and 1980. These portfolios contained the shares that had given the worst returns over a three-year period. The performances of these portfolios were then compared with the market as a whole over the subsequent three years. They found that these shares outperformed the market by an aver-age of 19.6 per cent in the next 36 months. Their explanation is that the market had overreacted to the bad news and undervalued the shares. Moreover, when portfolios of shares which had risen the most in the prior three years were constructed and followed for a further three years, they underperformed the market by 5 per cent. De Bondt and Thaler claim: ‘Substantial weak form market inefficiencies are discovered’, in their analysis. Chopra et al. (1992) carried out a more detailed study and concluded: ‘In portfolios formed on the basis of prior five-year returns, extreme prior losers outperform extreme prior winners by 5–10 per cent per year during the subsequent five years’. In a US and Hong Kong study, George and Hwang (2007) found losers outperforming winners by 0.56 per cent per month. Arnold and Baker (2007) investigated the return reversal phenomenon in UK shares. Our results show a stronger return reversal effect than that displayed in US shares. Every January between 1960 and 1998 we calculated for every share on the London Share Price Data (LSPD) its prior five-year return (capital gains plus dividends). The LSPD contains all the shares listed on the London Stock Exchange for the period 1975 to 2002. Before 1975 it contains share returns for a random one-third sample. Shares were ranked (an average of over 950 companies each January) in order of their five-year performance. They were then split into ten equal-sized groups (deciles) with group 1 containing the worst performers (‘losers’) over the prior five years, group 2 the next worst and so on, to group 10 (the ‘winners’). We then imagined buying each of the port-folios of shares and holding them for various periods up to 60 months. Returns, relative to the market index, were recorded. We found that the loser shares (on average, over 39 portfolio forma-tions, 1960–98) outperformed the winner shares by 14 per cent per year when held for five years. Furthermore, the 39 loser portfolios outperformed the market index by an average of 8.9 per cent per year over a five-year holding period. Exhibit 13.8 shows some of the results. The lines trace the cumulative return for each of the ten portfolios after allowing for the return on the market. The horizontal line at ‘0’ represents the market return re-based to zero throughout. The loser portfolios outperform the market by 53 per cent over five-year holding periods, or 8.9 per cent per year; whereas the winner portfolio, on average over 39 tests, underperforms the market by 47 per cent. Remarkably, all the other port-folios are in the ‘right’ order: 2 is above 3, 3 above 4, and so on. This lends considerable support to the view that investors overreact to poor news (e.g. declining profits) coming from ‘bad’ com-


Exhibit 13.7 (continued)

University, that the human eye is capable of detecting sophisticated and meaningful patterns in price charts which even the most sophisticated computer programmes cannot do. They show human beings can consistently distinguish between graphs of actual financial market returns and those generated at random. This opens up the intriguing possibility of harnessing the human skills of pattern recognition to computer-generated algorithms, which are designed

to counter the inconsistency and emotional biases to which human investors are also prone.

Even though the investing world has been transformed over the last generation, one has to conclude the exceptional power of the human brain to find meaning in complex patterns lives on. Investors who go on reading price charts, in any event, no longer need to apologise for their strange pastime.

Financial Times, 17 October 2010, p. 28.All Rights Reserved.

panies and good news coming from the stars, because the greatest extent of return reversal is in the most extreme prior-period return-ranked portfolios. The overreaction hypothesis states that investors push the losers down too far, and push the winners up too much, failing to allow suf-ficiently for the potential of losers to pick themselves up, and for the winners to make a mistake and fall off their pedestals, or, at least, to perform less well than expected. Exhibit 13.9 shows the difference in five-year test-period performance between the losers and the winners (losers minus winners) for each of the 39 portfolio formations separately. There are very few occasions when those companies considered star performers go on to generate better returns for investors than those widely regarded as the ‘dogs’. It might be thought that the results are explained by investors in loser shares taking on more risk than investors in winners. The study tests risk in six ways and failed to explain the outper-formance as a result of losers being more risky. The CAPM-beta of losers, for example, is shown to be less than that for winners. In a further study (Arnold and Xiao, 2007) an even better per-formance was achieved by selecting only those loser companies with strong financial variables such as positive cash flow, improving gearing (using financial strength variables found to indicate abnormal share returns by Piotroski (2000)).

Price (return) momentumMany professional fund managers and private investors follow a price momentum strategy when choosing shares. That is, they buy shares which have risen in recent months and sell shares that have fallen. The first major academic study in this area was by Jegadeesh and Titman (1993) who found that if you bought US shares that had performed well in the past few months while selling


10 (winner)

0

0.2

0.4

0.6

–0.2

–0.4

–0.60 10 20 30 40 50 60

Ave

rage

mar

ket-

adju

sted

buy

-and

-hol

d re

turn

Months since portfolio formation

9

87

65

43

2

1 (loser)

A figure of 0.4 should be interpreted as a cumulative return of 40% after allowance for the market return.

Source: Arnold and Baker (2007).

Exhibit 13.8 Cumulative market-adjusted returns for UK share portfolios constructed on the basis of prior five-year returns

shares that had performed poorly you would generate returns significantly exceeding those on the general market index for investment periods of three, six, nine and 12 months. For example, a strategy that selects shares on their past six-month returns and holds them for six months, realises a compounded return above the market of 12.01 per cent per year on average. Note that these results at first seem diametrically opposed to those of the return reversal, because the best strategy is to buy winners. However, the key to understanding the results and relating it to investor behav-iour is to realise that return reversal is a long-term phenomenon stretching over many years, whereas price momentum strategists look only to the returns over the prior three, six, nine or 12 months to select their extreme winners – and they do not hold for more than one year.5

Two explanations for price momentum are debated in the literature (apart from the view that the returns are explained by risk differences). The first is that investors underreact to new infor-mation. So, if a company has reported large increases in profits over the last six months the share price rises, but it does not rise enough fully to reflect all the new information. The argument runs that investors tend to ‘anchor’ beliefs about a company and so they are slow to realise that the company has entered an accelerated growth phase. They might, at first, anticipate it fizzling out, or even that the profit trend will go into reverse. However, as good news accumulates over time, increasing numbers of investors rerate the shares and push up the share price. On the other hand, when examining a stream of bad news from losers, they are at first reluctant to believe that the severity of the bad news will continue and therefore do not sell off the shares as much as market efficiency would imply. This means that as more news arrives they realise that they had previously underreacted, and so the share continues to fall.

5 There are some theoretical explanations for the co-existence of return reversal and momentum, e.g. Barberis et al. (1988), Daniel et al. (1998) and Hong and Stein (1999).


500

200

100

0

–100

–200

60

Year

Per

cent

ret

urn

over

5 y

ears

62 64 66 68 70 72 74 76 78 82 84 86 90 92 94 9688

300

400

80 98

The five-year test period returns for each loser-winner strategy are assigned to the year of formation.

Source: Arnold and Baker (2007).

Exhibit 13.9 Market-adjusted buy-and-hold five-year test-period returns for loser minus winner strategies for each of the 39 portfolio formations

The alternative theory is that investors are actually overreacting during the test (after purchase) period. After a series of months of rising prices investors jump on the bandwagon and push the share prices of winners to irrational levels, while selling off the losers unreasonably and so pushing their prices below the efficient level during the test period. The advocates of this argument point to the tendency of these winner and loser portfolios to show return reversal over the subsequent two years or so as proof of temporary overreaction. Perhaps both theories could have a role to play in explaining the price momentum effects found in share returns. Jegadeesh and Titman’s work was followed up with papers examining the phenomenon in stock markets around the world. For example, Rouwenhorst (1998) showed price momentum in 12 developed country stock markets, and then in a number of emerging stock markets (Rouwenhorst, 1999). In the UK Liu et al. (1999) demonstrated the effect for the period 1977–98, but doubt was cast on the likelihood that price momentum is a feature of the UK market at all times by the work of Hon and Tonks (2003), who showed that while momentum was a good strategy to follow in the 1980s and 1990s (which was mostly one long bull market) it produced poor returns in the previous two decades. To discover the extent to which price momentum is a reliable strategy, and whether it works better in bull or bear markets, Arnold and Shi (2005) tested the strategy over the period 1956 to 2001. Some of the results are shown in Exhibit 13.10. While over the whole study period, on aver-age, winners outperform losers by up to 9.92 per cent per year the strategy is fairly unreliable. There are long periods when the losers outperform the winners – an average monthly return of less than zero on the chart. We found no significant performance difference between bull and bear markets.6 Exhibit 13.11 shows that momentum trading has been taken to the extreme with computers used for automatic trades. Because it is so well known and many attempt to exploit it, perhaps the momentum phenomena will disappear under the weight of buy and sell orders (or is that £20 note still lying on the floor?). In a study going all the way back to 1900, Dimson et al. (2008) found momentum – see Exhibit 13.12.

Semi-strong form tests

The semi-strong form of efficiency has the greatest fascination for most researchers and practi-tioners. It focuses on the question of whether it is worthwhile expensively acquiring and analysing publicly available information. If semi-strong efficiency is true it undermines the work of millions of fundamental (professional or amateur) analysts whose trading rules cannot be applied to produce abnormal returns because all publicly available information is already reflected in the share price. Fundamental analysts try to estimate a share’s true value based on future business returns. This is then compared with the market price to establish an over- or under-valuation. To estimate the intrinsic value of a share the fundamentalists gather as much relevant information as possible. This may include macroeconomic growth projections, industry conditions, company accounts and announcements, details of the company’s personnel, tax rates, technological and social change and so on. The range of potentially important information is vast, but it is all directed at one objective: forecasting future profits and dividends. There are thousands of professional analysts constantly surveying information in the public domain. Given this volume of highly able individuals examining the smallest piece of news about a firm and its environment, combined with the investigatory and investment activities of millions of shareholders, it would seem eminently reasonable to postulate that the semi-strong form of EMH describes the reality of modern stock markets. The semi-strong form of EMH is threatening to share analysts, fund managers and others in the financial community because, if true, it means that they are unable to outperform the market aver-age return except by chance or by having inside knowledge. The great majority of the early evidence (1960s and 1970s) supported the hypothesis, especially if the transaction costs of special trading strategies were accounted for. The onus was placed on


6 Some other papers showing evidence of share price momentum: Sagi and Seasholes (2007), Figelman (2007), Chui et al. (2010) Fama and French (2008).


Exhibit 13.11

Many in the stock market put the increase in the number of wild share price movements down to the growth of computer-driven trading.

Many hedge funds and proprietary trading desks at leading investment banks run sophisticated software applications, sometimes known

as ‘sniffers’, which screen equities looking for momentum, effectively ‘sniffing’ out trading patterns.

‘The software doesn’t care what the stock is,’ a trader at a leading broker says of his company’s product.

Rising popularity of algorithmic tradeBy Phillip Stafford

6.00%

4.00%

2.00%

0.00%

–2.00%

–4.00%

–6.00%

–8.00%

Jul-5

6

Portfolio formation period

Mon

thly

ret

urns

, %

Jul-5

8Ju

l-60Ju

l-62Ju

l-64Ju

l-66Ju

l-68Ju

l-70Ju

l-72Ju

l-74Ju

l-76Ju

l-78Ju

l-80Ju

l-82Ju

l-84Ju

l-86Ju

l-88Ju

l-90Ju

l-92Ju

l-94Ju

l-96Ju

l-98Ju

l-00

Portfolios are constructed on six-month prior-period returns and held for six months. Buy-and-hold monthly returns over the six months for the winner portfolio minus the loser portfolio. Each portfolio formation is shown separately.

Source: Arnold and Shi (2005).

Exhibit 13.10 Price momentum

559Chapter 13 • Stock market efficiency�


‘It doesn’t look at anything else, such as price/ earnings ratios, it can just look at momentum in stocks.’

When a hedge fund or prop trader then submits their interest in a particular stock, it appears on

electronic order books, and the sheer volume of the order is soon noticed.

Thus, orders to buy or sell shares attract similar orders, fuelling a virtuous or vicious circle that causes very dramatic share price movements.

Financial Times, 23 April 2007, p. 19.All Rights Reserved.

Exhibit 13.12

Momentum investing in equity markets delivers ‘striking’ and ‘remarkably persistent’ excess returns, according to the most comprehensive study to date of the phenomenon.

Yet the study’s highly regarded authors, Elroy Dimson, Paul Marsh and Mike Staunton of the London Business School, confessed to being ‘puzzled’ by the findings, which fly in the face of a belief in efficient markets. ‘As a measure of abnormal performance it is quite striking,’ said Mr Dimson, BGI professor of investment management at LBS.

Mr Marsh, emeritus professor of finance, added: ‘It’s puzzling. You shouldn’t be able to make money that easily just by ranking stocks in order.’

The research, published as part of ABN Amro’s latest Global Investment Returns Yearbook, used data series stretching back to 1900 to examine the efficacy of systematic momentum investing; simply

buying the stocks that had performed best over a prior time period and shorting those that had performed worst.

For example, using data on the UK’s 100 largest stocks since 1900, the team created two portfolios, one based on the 20 best-performing equities in the previous 12 months and the other the 20 worst performers. These portfolios were then re-calculated every month. The portfolio of winners produced compound annual returns of 15.2 per cent, turning £1 into more than £4.2m by the end of 2007. In contrast, the portfolio of laggards returned just 4.5 per cent a year, turning £1 into £111.

The gulf was wider when the team used data from the entire London market since 1955; the portfolio based on the previous 12 months’ best-performing stocks returned 18.3 per cent a year, against 6.8 per cent for the erstwhile worst performers. When the

‘Ignore momentum at your peril’By Steve Johnson

Source: ABN Amro/London Business School * Winners and losers in previous 12 months, portfolio rebalanced monthly

10,000,000

1900 10 20 30 40 50End of year

60 70 80 90 2000

1,000,000

‘Winners’

‘Losers’

100,000

10,000

1,000

100

10

1

0

Momentum effect in the UKIndex value

Momentum effect globally2000–07 ‘Winners Minus Losers’ premium (% per annum)

2Spain

4US

6Japan

6Italy

11UK

14Switzerland

20Netherlands

21France

23Sweden

34Canada

39Germany

42Australia

Marketaverage

Compound annual returns‘Winners*’ 15.2%Market average 9.4%‘Losers*’ 4.5%

those who believed that the market is inefficient and misprices shares to show that they could perform extraordinarily well other than by chance. As Exhibit 13.13 makes clear most of these professionals have performed rather poorly. Remember that simply by chance you would expect 50 per cent of them to outperform the market index before fees.



portfolios were constructed on an equal-weighted, rather than market cap-weighted basis, the divide was starker.

This momentum premium also appeared to hold across borders; Messrs Dimson, Marsh and Staunton found positive returns from each of the 16 other countries they crunched post-2000 data for, with winners outperforming losers by 4 per cent a year in the US, 21 per cent in France and 39 per cent in Germany.

An investment approach based on this strategy – buying top performing stocks and shorting the laggards – would not be straightforward to execute. The research found portfolios need to be turned over regularly, adding to transaction costs, while smaller stocks may be difficult to short.

However, the academics argue it has widespread repercussions, with virtually every investment

manager either betting in favour of momentum – from trendfollowing hedge fund managers to long-only managers who let winners run while cutting losers and engaging in ‘window dressing’ of their portfolio – or, in the shape of small cap or value investors, betting against it.

‘Every investor we have come across has, explicitly or implicitly, used a momentum or counter-momentum strategy,’ said Prof Marsh. ‘[Consequently] people can look like a genius by accident or they can look a fool when they are quite smart.

‘Active managers who ignore the momentum effect do so at their peril.’

‘It is a very simple strategy, buying winners and selling losers. In a well-functioning market it ought not to work,’ said Prof Marsh. ‘We remain puzzled and we are not the only ones; most academics are vaguely embarrassed about this.’

Financial Times, 18 February 2008, FTfm p. 1.All Rights Reserved.

Exhibit 13.13

. . . Legal & General summarises much of the [fund management] data. It shows that over five years the FTSE All Share index outperformed 55 per cent of actively managed funds investing in UK shares – before fees. After high initial fees, just a quarter of funds managed to beat the index.

Investors would have made more money by backing the index and aiming for market returns than investing with most individual managers, says L&G. The chances of picking a UK fund that will continue to turn in an index-beating return over five years is considerably less than one in five. Of the 72 active funds that outperformed the index in 1998 just 31 active funds were still doing so in 2003.

The longer term statistics are just as bad. Of the 44 actively managed trusts with a 20-year performance

history, eight outperformed the FTSE All-Share index, highlighting just how difficult it is to identify which trusts will be long-term winners, said the WM Company, which assesses performance.

Those funds that do outperform often do so because they are taking higher risks than other funds, say academics, and are unlikely to sustain that outperformance over successive years. If there is any evidence of persistent outperformance it is due to luck and momentum rather than judgement – that is, a fund holds a stock that produces a high return in one year and carries on to the next.

Once you adjust returns for risk that the managers have taken, the only evidence of consistency is on the downside – that is, poor managers systematically underperform benchmarks.

Fund managersIt does not always pay to follow the starsKate Burgess

The fundamental analysts have not lost heart, and have fought back with the assistance of some aca-demic studies which appear to suggest that the market is less than perfectly efficient. There are some anomalies which may be caused by mispricing. For example, small firm shares have performed abnor-mally well (for certain periods) given their supposed risk class, and ‘value investing’ seems to produce unexpectedly high returns. We will now discuss some of the evidence for and against semi-strong efficiency.7

Seasonal, calendar or cyclical effectsNumerous studies have identified apparent market inefficiencies on specific markets at particu-lar times. One is the weekend effect, in which there appear to be abnormal returns on Fridays and relative falls on Mondays. The January effect refers to the tendency for shares to give excess returns in the first few days of January in the USA. Some researchers have found an hour of the day effect in which shares perform abnormally at particular times in the trading day. For example, the first 15 minutes have given exceptional returns, according to some studies. The problem for practical investment with placing too much importance on these studies is that the moment they are identified and publicised there is a good chance that they will cease to exist. Investors will buy in anticipation of the January effect and so cause the market already to be at the new higher level on 1 January. They will sell on Friday when the price is high and buy on Monday when the price is low, thus eliminating the weekend effect. Even if the effects are not eliminated trading strategies based on these findings would be no more profitable than buying and holding a well-diversified portfolio. This is because of the high transaction costs associated with such strategies as, say, buying every Tuesday and selling every Friday. Also the research in this area is particularly vulnerable to the accusation of ‘data-snooping’. Sullivan et al. (1999) claim to demonstrate that calendar effects are illusory and findings obtained merely the result of extensive mining of the data until an (apparent) relationship is found:

Data-snooping need not be the consequence of a particular researcher’s efforts. It can result from a subtle survivorship bias operating on the entire universe of technical trading rules that have been con-sidered historically. Suppose that, over time, investors have experimented with technical trading rules drawn from a very wide universe – in principle thousands of parameterizations of a variety of types of rules. As time progresses, the rules that happen to perform well historically receive more attention and are considered ‘serious contenders’ by the investment community, and unsuccessful trading rules are more likely to be forgotten. After a long sample period, only a small set of trading rules may be left for consideration, and these rules’ historical track records will be cited as evidence of their merits. If enough trading rules are considered over time, some rules are bound by pure luck, even in a very large sample, to produce superior performance even if they do not genuinely possess predictive power over asset returns. Of course, inference based solely on the subset of surviving trading rules may be mislead-ing in this context because it does not account for the full set of initial trading rules, most of which are likely to have underperformed.

7 This is an area with an enormous literature. The References and further reading section at the end of the chapter contains some of the EMH papers.



‘Losers generally repeat, while winners do not necessarily repeat,’ was the bleak summation reached by professors David Blake of Birkbeck

College and Allan Timmerman of the University of California in a study for the Financial Services Authority . . .

Financial Times, 15/16 May 2004, p. M23.All Rights Reserved.

Small firmsThe searchers for inefficiency seemed to be on firmer ground when examining smaller firms. The problem is that the ground only appears to be firm until you start to build. A number of studies in the 1980s found that smaller firms’ shares outperformed those of larger firms over a period of several decades (the small firm effect, small-capitalisation, or small-cap effects). This was found to be the case in the USA, Canada, Australia, Belgium, Finland, the Netherlands, France, Germany, Japan and Britain.8 Dimson and Marsh (1986) put the outperformance of small UK firms’ shares at just under 6 per cent per year. These studies caused quite a stir in both the aca-demic and the share-investing communities. Some rational explanations for this outperformance were offered: for example, perhaps the researchers had not adequately allowed for the extra risk of small shares – particularly the risk associated with lower liquidity. In most of these studies beta is used as the measure of risk and there are now doubts about its ability to capture all the risk-return relationship (see Chapter 8). Besides, the results generally show lower betas for small companies. Some researchers have argued that small firms suffer more in recessions and so can be judged as more risky. Another explanation is that it is proportionately more expensive to trade in small companies’ shares: if transaction costs are included, the net return of trading in small company shares comes down (but this does not explain the outperformance of a portfolio bought and held for a long period). There is also the issue of ‘institutional neglect’, by which analysts fail to spend enough time studying small firms, preferring to concentrate on the larger 100 or so. This may open up opportunities for the smaller investor who is prepared to conduct a more detailed analysis of those companies to which inadequate professional attention is paid. The excitement about small companies’ shares by investors and their advisers was much greater than in academe, but it was to end in tears. Investors who rushed to exploit this small firm effect in the late 1980s and early 1990s had their fingers burnt. As The Economist9 put it: ‘The suppos-edly inefficient market promptly took its revenge, efficiently parting investors from their money by treating owners of small stocks to seven years of under-performance.’ This article refers to the US market but similar underperformance occurred on both the US and UK markets. UK studies by Dimson, Marsh and Staunton (Dimson and Marsh 1999, Dimson et al., 2001, 2002) showed that smaller companies outperformed large companies by 5.2 per cent per annum between 1955 and 1988 (by 4.5 per cent for small companies and 9.0 per cent for very small (micro) companies). However, in the period 1989 to 1998 the return premium in favour of small compa-nies went into reverse: large companies produced a return 7.0 per cent greater than small companies and 10.5 per cent for micro capitalisation companies – see Exhibit 13.14. (The research periods for the USA are 1926 to 1983, and then 1984 to 1998.) The researchers show that this kind of reversal occurred in many different countries in the late 1980s and 1990s. Some people say that what hap-pened was that following the early 1980s’ academic studies so many funds were set up to buy small firms’ shares that in 1986 and 1987 their prices were pushed up to unsustainable levels (they had 10 years of outperformance pushed into two). Indeed in the ten years to the end of 2009 small UK firms returned 3.2 per cent more than FTSE All-share firms. This return of the small-cap effect is global: over the decade to the end of 2010 companies in the worldwide small cap index outperformed those in the index for all companies by 7 per cent per annum.10

Underreaction Research evidence is building which shows that investors are slow to react to the release of infor-mation in some circumstances. This introduces the possibility of abnormal returns following the announcement of certain types of news. The first area of research has been into ‘post-earnings-announcement drift’. That is, there is a sluggish response to the announcement of unexpectedly good or unexpectedly bad profit figures. Bernard and Thomas (1989) found that cumulative


8 Key studies in the area are Banz (1981), Reinganum (1981), Keim (1983), Fama and French (1992), Dimson, et al. (2002) and the annual Hoare Govett Smaller Companies Index reports.

9 The Economist, 26 March 1994.10 Sullivan, R. (2011) ‘Why it’s wise to stick with global small caps’, Financial Times, 16 January.

abnormal returns (CARs) continue to drift up for firms that report unexpectedly good earn-ings and drift down for firms that report unexpectedly bad figures for up to 60 days after the announcement. (The abnormal return in a period is the return of a portfolio after adjusting for both the market return in that period and risk.) This offers an opportunity to purchase and sell shares after the information has been made public and thereby outperform the market returns. Shares were allocated to 10 categories of standardised unexpected earnings (SUE). The 10 per cent of shares with the highest positive unexpected earnings were placed in category 10. (The worst unexpected return shares were placed in category 1.) Exhibit 13.15 shows that after the announce-ment the shares of companies in category 10 continue to provide positive CARs. Investors did not move the share price sufficiently to incorporate the new information in the earnings announce-ment on the day of the announcement. Those reporting bad surprises in earnings (the worst of which were in category 1) continued to show a falling return relative to the market in the period after the announcement day. Bernard and Thomas say that a strategy of buying shares in category 10 and selling shares in category 1 on the announcement day and selling (buying) 60 days later would have yielded an estimated abnormal return of approximately 4.2 per cent over 60 days, or about 18 per cent on an annualised basis. Similar results have been reported in studies by Foster et al. (1984), Rendleman et al. (1982), Liu et al. (2003), Lerman et al. (2007), Hirshleifer et al. (2009), Dellavigna and Pollet (2009), Chordia et al. (2009) and Kama (2009). These studies sug-gest that all the news is not properly priced into the shares at the time of announcement as would be expected under EMH. The second area of research into underreaction relates to the repurchase of shares. Ikenberry et al. (1995) found that share prices rise on the announcement that the company will repurchase its own shares. This is to be expected as this is generally a positive piece of news. The suggestion of inefficiency arises because after the announcement the shares continue to provide abnormal returns over the next few years. Thirdly, Michaely et al. (1995) and Liu et al. (2008) found evi-dence of share price drift following dividend initiations and/or omissions. Fourthly, Ikenberry et al. (1996) found share price drift after share split announcements. Fifthly, we have already discussed an underreaction to past price movements (a ‘price momentum effect’).


Source: Dimson, E., Marsh, P.R. and Staunton, M. (2002) Triumph of the Optimists: 101 Years of Global Investment Returns. Princeton, NJ: Princeton University Press.

United Kingdom

–10.5

–7.0

United States

–6.4

–2.8

United Kingdom

9.0

4.5

United States

3.92.2

–0

2

4

6

8

10

–2

–4

–6

–8

–10

–12

US small-caps

US micro-caps

UK small-caps

UK micro-caps

Annualised percentage size premium (extra annual return)

End of honeymoon period to end of 1998Research period to end of honeymoon period

Exhibit 13.14 The small-cap reversal in the United States and the United Kingdom

Value investingThere is a school of thought in investment circles that investors should search for ‘value’ shares. Different sub-schools emphasise different attributes of an undervalued share but the usual candi-dates for inclusion are:

� a share with a price which is a low multiple of the earnings per share (low P/E ratios or PERs);

� a share price which is low relative to the balance sheet assets (book-to-market ratio);

� a share with high dividends relative to the share price (high-yield shares).11

11 The doyen of the value investing school, Benjamin Graham, regarded the use of a single measure in isola-tion as a very crude form of value investing. In fact, he would condemn such an approach as not being a value strategy at all. See Security Analysis by Graham and Dodd (1934) and The Intelligent Investor (1973) by Graham (reprinted 2003).


SUEdeciles

post-announcement period

109876

5

4

321

SUEdeciles

pre-announcement period

10

9

8

76

5

4

3

2

1

0

2

4

6

–2

–4

–6

–8–60 –40 –20 0 0 20 40 60

CA

R (

%)

event time in trading days relative to earnings announcement day

Note: The 10 per cent of shares with the most positive unexpected news continued to produce abnormal returns after the announcement day, whereas the 10 per cent with the worst news continued to produce negative abnormal returns cumu-lating to over 2 per cent.

Source: Bernard, V. and Thomas, J., ‘Post-earnings-announcement drift: Delayed price response or risk premium?’, Journal of Accounting Research, 27 (1989, Supplement), p. 10.

Exhibit 13.15 The cumulative abnormal returns (CAR) of shares in the 60 days before and the 60 days after an earnings announcement

We turn first to the purchase of low price-earning ratio shares as an investment strategy. The evi-dence generally indicates that these shares generate abnormal returns. Basu (1975, 1977, 1983), Keim (1988) and Lakonishok et al. (1994) have produced evidence which appears to defy the semi-strong EMH, using US data. Mario Levis (1989), Gregory et al. (2001, 2003), Anderson and Brooks (2006) and Li et al. (2009) found exceptional performance of low PER shares in the UK. For Sweden, Hamberg and Novak (2010) and for Japan, Chan et al. (1991) report similar find-ings. The academic literature tends to agree that low PER shares produce abnormal returns but there is some dispute whether it is the small-size effect that is really being observed; when this factor is removed the PER effect disappears, according to Reinganum (1981) and Banz and Breen (1986). Doubts were raised because small firm shares are often on low PERs and so it is difficult to disentangle the causes of outperformance. Jaffe et al. (1989), based on an extensive study of US shares over the period 1951–86, claimed that there was both a price–earnings ratio effect and a size effect. However, the results were contradicted by Fama and French (1992), who claim that low PER shares offer no extra return but that size and book-to-market ratio are determining fac-tors. On British shares Levis (1989) and Gregory et al. (2001) distinguished between the size and PER effects and concluded that low PERs were a source of excess returns. One explanation for the low PER anomaly is that investors place too much emphasis on short-term earnings data and fail to recognise sufficiently the ability of many poorly performing firms to improve. Investors seem to put some companies on a very high price relative to their current earnings to reflect a belief in rapid growth of profits, while putting firms with declining profits on unreasonably low prices. The problem is that the market apparently consistently overprices the ‘glamour’ shares and goes too far in assigning a high PER because of overemphasis on recent performance, while excessively depressing the share prices of companies with low recent earnings. To put it crudely: so much is expected of the ‘glamour’ shares that the smallest piece of bad news (or news that is less good than was expected) brings the price tumbling. On the other hand, so little is expected of the historically poor performers that good news goes straight into a share price rise. What investors have failed to appreciate is the tendency for extreme profit and growth trends to moderate – ‘to revert to the mean’. This was shown in research by Little as early as 1962. He described profit differences from one period to another as higgledy piggledy growth. Fuller et al. (1993) found that portfolios constructed from shares with low PERs showed lower profit growth than portfolios of high PERs shares in each of the eight years after portfolio formation. However, after three to four years the growth rate differences became very small. If investors were buying high PER shares because they expected high earnings growth for decades into the future (thus bidding up the price) they were frequently disappointed. On the other hand, investors buying low PER shares when the price is low because most investors believe the company is locked into low earnings growth found, after three or four years, that the earnings of these companies, on average grew at very nearly the same rate as the glamour shares. Dreman (1998) is a leading investor who has written on the tendency for investors to overreact and bid up glamour shares too far – while neglecting other companies. The efficient market protagonists (e.g. Fama and French) have countered the new evidence of inefficiency by saying that the supposed outperformers are more risky than the average share and therefore an efficient market should permit them to give higher returns. Lakonishok et al. (1994) examined this and found that low PER shares are actually less risky than the average. Before everyone rushes out to buy low PER shares remember the lesson that followed the dis-covery of a small firm effect in the mid-1980s. Does it still exist? Shares that sell at prices which are a low multiple of the net assets per share (i.e. high book-to-market ratio) seem to produce abnormal returns.12 This seems odd because (as we discuss in Chapter 17) the main influence on most share prices is the discounted value of their future income flows. Take BSkyB which had a mere £1bn of net assets in 2011 and is valued in the stock market at over £12bn. Its assets are largely intangible and not adequately represented in a balance sheet. In other words, there is very little connection between balance sheet asset figures and share price


12 For example Lakonishok et al. (1994), Chan et al. (1991), De Bondt and Thaler (1987), Rosenberg et al. (1985), Fama and French (1992, 2006), Capaul et al. (1993), Pontiff and Schall (1998), Reinganum (1988), Gregory et al. (2001, 2003) Dimson et al. (2002), Lewellen (2004), Phalippou (2008), Fama and French (2008), Michou (2009) and Shon and Zhou (2010).

for many shares. The causes of the results of the empirical studies remain largely unexplained. Fama and French (1992, 2006) suggest there may be a systematic difference between companies which have high or low book-to-market value ratios. That is, companies with high book-to-market ratios are more risky. However, company shares have high market price-to-book value for different reasons – for some the nature of their industrial sector means they have few balance sheet record-able assets, for some the share price has risen because of projections of strong earnings growth. It has been suggested that investors underprice some shares in an overreaction to a series of bad news events about the company, while overpricing other shares that have had a series of good news events. Thus, the book-to-market ratio rises as share prices fall in response to an irrational extrapo-lation of a bad news trend. Many studies have concluded that shares offering a higher dividend yield tend to outperform the market.13 Explanations have been offered for this phenomenon ranging from the fact that div-idend income is taxed at a higher rate than capital gains and so those investors keen on after-tax income will only purchase high-yielding shares if they offer a higher overall rate of return, to the argument that investors are bad at assessing growth prospects and may underprice shares with a high dividend yield because many have had a poor recent history. Two other value measures have been examined. The first is the share price to sales; high sales-to-price ratio firms perform better than low sales-to-price firms. Secondly, there is the cash flow (defined as profits after tax plus depreciation and amortisation) to price ratio. Lakonishok et al. (1994) showed a higher return to shares with a high cash flow to price ratio.

BubblesOccasionally financial and other assets go through periods of boom and bust. There are explosive upward movements generating unsustainable prices, which may persist for many years, followed by a crash. These bubbles seem at odds with the theory of efficient markets because prices are not supposed to deviate markedly from fundamental value. The tulip bulb bubble (tulipmania) in seventeenth-century Holland is an early example in which tulip bulb prices began to rise to absurd levels. The higher the price went the more people considered them good investments. The first investors made lots of money and this encouraged others to sell everything they had to invest in tulips. As each wave of speculators entered the market the prices were pushed higher and higher, eventually reaching the equivalent of £30,000 in today’s money for one bulb. But the fundamentals were against the investors and in one month, February 1637, prices collapsed to one-tenth of the peak levels (by 1739 the price had fallen to 1/200th of one per cent of its peak value). The South Sea Bubble which burst in 1720 was a British share fiasco in which investors threw money at the South Sea Company on a surge of over-optimism only to lose most or all of it. The increase in share prices in the 1920s and before the 1987 crash have also been interpreted as bubbles. More recently, the mania for telecoms, media and technology shares in the late 1990s has been identified as leading to a bubble. I wrote in the fourth edition that ‘many see property prices in 2007 in many countries as being determined largely by a bubble mentality’ – hindsight con-firms this (see Kindleberger and Aliber (2011) for more on bubbles). One explanation for this seemingly irrational behaviour of markets is what is called noise trad-ing by naive investors. According to this theory there are two classes of traders, the informed and the uninformed. The informed trade shares so as to bring them towards their fundamental value. However, the uninformed can behave irrationally and create ‘noise’ in share prices and thereby generate bias in share pricing. They may be responding to frenzied expectations of almost instant wealth based on an extrapolation of recent price trends – perhaps they noted from the newspapers that the stock market made investors high returns over the past couple of years and so rush to get a piece of the action. This tendency to ‘chase the trend’ can lead to very poor performance because the dabbler in the markets often buys shares after a sharp rise and sells shares after being shocked by a sharp fall.


13 For example Litzenberger and Ramaswamy (1979), Elton et al. (1983), Levis (1989), Morgan and Thomas (1998), Miles and Timmermann (1996), Dimson et al. (2002) and Lewellen (2004).

To reinforce the power of the uninformed investor to push the market up and up, the informed investor seeing a bubble developing often tries to get in on the rise. Despite knowing that it will all end in disaster for some, the informed investor buys in the hope of selling out before the crash. This is based on the idea that the price an investor is willing to pay for a share today is dependent on the price the investor can sell for at some point in the future and not necessarily on fundamental value. Keynes (1936) as far back as the 1930s commented that share prices may not be determined by fun-damentals but by investors trying to guess the value other investors will place on shares. He drew the analogy with forecasting the outcome of a beauty contest. If you want to win you are better off concentrating on guessing how the judges will respond to the contestants rather than trying to judge beauty for yourself. George Soros is an example of a very active (informed and successful) inves-tor who is quite prepared to buy into an apparent irrational market move but makes every effort to get out before the uninformed investors. There is evidence that a number of hedge funds rode the technology share bubble in the late 1990s. They did not act as a correcting force returning prices to efficient levels, but reinforced the bubble in a destabilising way (see Brunnermeier and Nagel, 2004). Note that the term ‘informed investor’ does not equal professional investor. There are many profes-sional fund managers and analysts who, on a close examination, fall into the category of ill-informed noise traders (see Arnold (2009) for more on the inadequacies of ‘professional investors’, also known as ‘the oxymorons’, and Arnold (2010) for an explanation of George Soros’ reflexivity theory). Exhibit 13.16 puts forward the case that the incentives in investing institutions encourage bubbles.


Exhibit 13.16

Now that even the UK’s chief financial regulator has started kicking the theory of efficient markets, the fun has gone out of it somehow. Moderation in all things: and it is worth remarking that just because markets are inefficient, that need not mean investors are irrational.

The point is contentious, and needs defending. As it happens, I have come across an academic paper which does just that. But first, an observation.

Some economists insist that markets must be efficient because they are rational. And if they are not rational, the whole of economic theory collapses.

So be it, we might reply. Better no theory than a dud one. And other theories, based on behaviour rather than rationality, do a better job.

But when it comes to the big stuff, our actions belie that. When we are grappling with the subprime debacle or Chinese economic policy, we ask ourselves what people are up to – not how they behave, but how they are reasoning.

In fact, the theory of rational behaviour is a model – a schematic attempt to portray the big picture. Any such model, in or out of economics, contains

anomalies. To dwell on those anomalies, as we all now enjoy doing, risks missing the point. It is only when there are enough of them that the model is more hindrance than help and must be dumped.

So to the paper, by two academics from the London School of Economics – former hedge fund manager Paul Woolley and Professor Dimitri Vayanos. In effect, they argue that markets are inefficient for perfectly rational reasons.

The key lies in the use of agents. Conventional economic theory deals with representative individuals. But in reality, those individuals generally hand their savings to institutions, who hand them on to specialist fund managers.

If those managers underperform the market, it is hard for the investor to know whether they are deliberately avoiding overvalued stocks, or simply messing up. If the situation persists, then investors infer the latter and switch their money.

The germ of the idea came to Dr Woolley a decade ago, he says, when he was running the European end of the US hedge fund GMO. The fund relied on fundamental value during the dotcom boom,

Individual rationality can mean collective irrationalityBy Tony Jackson

�

Comment on the semi-strong efficiency evidenceDespite the evidence of some work showing departures from semi-strong efficiency, for most investors most of the time the market may be regarded as efficient. This does not mean the search for anomalies should cease. The evidence for semi-strong efficiency is significant but not so over-whelming that there is no hope of outperformance for the able and dedicated. While the volume of evidence of pockets of market inefficiencies is impressive, we need to be wary when placing weight on these results. Given the fact that there have been hundreds of researchers examining the data it is not a surprise that some of them find plausible looking statis-tical relationships indicating excess return opportunities. The research that actually gets published tends to be that which has ‘found’ an inefficiency. The research that does not show inefficiency receives much less publicity. Furthermore, the excess return strategies may be time specific and may not continue into the future. On the other hand consider this: suppose that you discover a trading strategy that produces abnormal returns. You could publish it in a respected journal or you could keep it to yourself. Most would select the latter option because publishing may result in the elimination of the inef-ficiency and with it the chance of high investment returns. So there may be a lot of evidence of inefficiency that remains hidden and is being quietly exploited. There is a strange paradox in this area of finance: in order for the market to remain efficient there has to be a large body of investors who believe it to be inefficient. If all investors suddenly believed that shares are efficiently priced and no abnormal profits are obtainable they would quite sensibly refuse to pay for data gathering and analysis. At that moment the market starts to drift away from fundamental value. The market needs speculators and long-term investors continually on the prowl for under- or overpriced securities. It is through their buying and selling activities that inefficiencies are minimised and the market is a fair game. Among academics and their intellectual disciples there was a high degree of faith in the EMH in the 1970s, 1980s and 1990s. Today that faith is slipping – see Exhibit 13.17. This will have pro-found implications for investment behaviour and corporate finance. There are some investors who have rejected the strictures of the EMH and have achieved aston-ishingly good returns. Here are some of them.14



when high-tech, telecom and media stocks became grossly overvalued. GMO initially shunned them, thereby underperforming hugely, and by the peak of the frenzy had lost 40 per cent of its funds under management. Until the tide turned, the only way it could stem the flow was to devote part of its funds (and the bulk of its trading) to momentum plays – that is, to the overvalued stuff.

The concept of momentum is important because efficient market theory says it should not exist. If investors decide a stock or sector is worth a price different from the present one, they should move to that price immediately.

In reality, of course, prices overshoot over long periods, then go into reverse. The dotcom example illustrates why.

As investors were bailing out of value funds such as GMO, they were gradually switching more cash into the bubble stocks. Thus those stocks

were pushed up and value stocks pushed down. Why gradually? Because it takes time to sack a fund manager and because individual investors capitulate at different levels.

In a sense, this is not new. I myself have banged on for years about how fundamental value is in practice irrelevant, since fund managers who stick to the fundamentals risk losing their jobs.

But I had rather assumed that was because end-investors were behaving irrationally. This thesis suggests otherwise.

The information gap between them and their agents means they are making the best of the knowledge available to them. So it turns out that individually rational actions add up to a collectively irrational outcome. That might seem odd to mainstream economists, but not to the rest of us. Mutually destructive wars have been fought on the same basis.

Financial Times, 31 August 2009, p. 16.All Rights Reserved.

14 These performances and the underlying investment philosophies are explored more fully in Arnold (2009) and in Arnold (2010).

Peter LynchFrom May 1977 to May 1990 Peter Lynch was the portfolio manager of Fidelity’s Magellan Fund. Over this 13-year period a $1,000 investment rose to be worth $28,000, a rate of return that is way ahead of the field at 29.2 per cent per annum. Furthermore, the fund’s performance was consist-ent – in only two of those years did it fail to beat the S&P 500. The fund grew from an asset base of $18m to one of more than $14bn. It was not only the best-performing fund in the world; it also became the biggest. There were one million shareholders in 1990, when Lynch quit, at the age of 46, to devote more time to his family. His experiences as a young man gave him a sceptical eye to what was being taught on his MBA course at Wharton:

It seemed to me that most of what I learned at Wharton, which was supposed to help you succeed in the investment business, could only help you fail . . . Quantitative analysis taught me that the things I saw happening at Fidelity couldn’t really be happening. I also found it difficult to integrate the efficient market hypothesis . . . It also was obvious that the Wharton professors who believed in quantum analysis and random walk weren’t doing nearly as well as my new colleagues at Fidelity, so between theory and practice, I cast my lot with the practitioners . . . My distrust of theorizers and prognosticators continues to the present day.15


Exhibit 13.17

A new realisation has dawned among the most fervent advocates of financial analysis and collective investor wisdom: markets are not always rational.

For the past five decades, the Chartered Financial Analyst Institute has been teaching the tenets of analysis based on efficient markets to tens of thousands of adherents from banks, fund managers and investment houses that make up the global financial system.

Now, however, the credit crisis has forced high priests of rational market theory to question their own creed.

The British CFA recently asked members for the first time whether they trusted in ‘market efficiency’ – and discovered more than two-thirds of respondents no longer believed market prices reflect all available information. More startling, 77 per cent of the group ‘strongly’ or ‘very strongly’ disagreed that investors behaved ‘rationally’ – in apparent defiance of the ‘wisdom of crowds’ idea that has driven investment theory.

The shift is significant as the assumption of efficient markets is a cornerstone of calculating the value of everything from stocks to pension fund liabilities to executive compensation.

William Goodhart, UK chief executive of the CFA, yesterday admitted the results showed a new mood of ‘questioning’ following the financial crisis.

However, the trend appears to reflect a wider intellectual swing. In the past three decades, the global asset management industry has been dominated by the so-called ‘efficient markets’ hypothesis, which has given birth to ideas such as the capital asset pricing model, that portrays investing as a trade-off between risk and return.

Extremities of recent market swings have sparked interest among politicians and investors in the field of behavioural finance, which asserts that markets do not behave rationally but can be driven by human emotions such as fear.

However, the CFA survey suggests the finance industry is not yet ready to rip up its creed.

Credit crunch causes analysts to rethink rational market theoryBy Gillian Tett, Capital Markets Editor

Financial Times, 16 June 2009, p. 17.All Rights Reserved.

15 Lynch (1990), pp. 34–5.

John NeffWhen John Neff managed the Windsor Fund, his investment philosophy emphasised the impor-tance of a low share price relative to earnings. However, his approach required a share to pass a number of tests besides the price–earnings criteria. These additional hurdles turn his approach from simple low price–earnings investing to a sophisticated one. John Neff was in charge of the Windsor Fund for 31 years. It beat the market for 25 of those 31 years. He took control in 1964, and retired in 1995. Windsor was the largest equity mutual in the United States when it closed its doors to new investors in 1985. Each dollar invested in 1964 had returned $56 by 1995, compared with $22 for the S&P 500. The total return for Windsor, at 5,546.5 per cent, outpaced the S&P 500 by more than two to one. This was an additional return on the market of 3.15 percentage points a year after expenses. Before expenses the outperformance was 3.5 percentage points.16 He was always on the lookout for out-of-favour, overlooked or misunderstood stocks. These nuggets of gold always stood on low price–earnings ratios. Not only that; their prospects for earnings growth were good. He believes that the market tends to allow itself to be swept along with fads, fashions and flavours of the month. This leads to overvaluation of those stocks regarded as shooting stars, and to the undervaluation of those which prevailing wisdom deems unexciting, but which are fundamentally good stocks. Investors become caught in the clutch of group-think and en masse ignore solid companies. Bad news tends to weigh more heavily than good news as the investor’s malaise deepens. The way Neff saw it, if you could buy a stock where the negatives were largely known, then any good news that comes as a surprise can have a profoundly positive effect on the stock price. On the other hand if you buy into a growth story where great things are expected and built in to the price, the slightest hint of bad news can take the sizzle out of the stock.

Benjamin GrahamBenjamin Graham is regarded as the most influential of investment philosophers. Graham was the leading exponent of the value investing school of thought. Over 20 years (from 1936) the Graham-Newman Corporation achieved an abnormally high performance for its clients: ‘The success of Graham-Newman Corporation can be gauged by its average annual distribution. Roughly speaking, if one invested $10,000 in 1936, one received an average of $2,100 a year for the next twenty years and recovered one’s original $10,000 at the end.’17 Graham18 in 1955 put a slightly different figure on it: ‘Over a period of years we have tended to earn about 20 per cent on capital per year’. This is much better than the return available on the market as a whole. For example, Barclays Capital19 show the annual average real (excluding inflation) rate of return on US stocks with gross income reinvested as 7.4 per cent for those years. Even if we add back average annual inflation of 3.8 per cent20 to the Barclays’ figures to make them comparable, the Graham-Newman figures are much better than the returns received by the average investor. According to Graham, market prices are not determined by any necessarily rational or mathematical relationship to fundamental factors (at least, not in the short run) ‘but through the minds and decisions of buyers and sellers’;21 ‘The prices of common stock are not carefully thought out computations, but the resultants of a welter of human reactions. The stock market is a voting machine rather than a weighing machine. It responds to factual data not directly, but only as they affect the decisions of buyers and sellers.’22

Plainly, he did not believe the efficient markets hypothesis:

Evidently the processes by which the securities market arrives at its appraisals are frequently illogical and erroneous. These processes . . . are not automatic or mechanical, but psychological for they go on in the minds of people who buy and sell. The mistakes of the market are thus the mistakes of groups of masses of individuals. Most of them can be traced to one or more of three basic causes: exaggeration, oversim-plification, or neglect.23


16 Neff (1999), pp. 62 and 71.17 Train (1980), p. 98.18 Reproduced in Lowe (1999), p. 116.19 Barclays Capital, Equity Gilt Studies (published annually).20 Implicit price deflation for GNP. US Office of Business Economics, The National Income and Product

Accounts of the United States 1929–1965.21 Graham and Dodd (1934), p. 12.22 Graham and Dodd (1934), p. 452.23 Graham and Dodd (1934), p. 585.

Warren Buffett and Charles MungerWarren Buffett is the most influential investment thinker of our time; he is also the wealthiest. Charles Munger is Buffett’s partner, both intellectually and in the running of one of the world’s largest companies. They each started with very little capital. At first, they developed their investment philosophies independently. They were far away from each other, both in their investment approach and geographically (Munger in California and Buffett in Nebraska). Despite the different approaches to stock picking they each created highly successful fund management businesses before coming together. Buffett took managerial control of Berkshire Hathaway in 1965 when the book value per share was $19.46 (as measured at the prior year-end 30 September 1964). By year-end 2011 the book value, with equity holdings carried at market value, was $99,860 per share. The gain in book value over 47 years came to 19.8 per cent compounded annually. At this rate of return an investment of $100 becomes worth over $513,053 over 47 years. There are people who are multimillionaires today because in the 1960s or 1970s they invested a few thousand dollars in Berkshire Hathaway. Warren Buffett owns around 40 per cent of Berkshire Hathaway,24 a company with a market capitalisation of over $200bn (it was valued at a mere $20m in 1965). Exhibit 13.18 shows the truly outstanding performance of Berkshire Hathaway. There have been only eight years in which the rise in book value was less than the return on the S&P 500. It is even better than it looks – the S&P 500 numbers are pre-tax whereas the Berkshire numbers are after-tax! Berkshire owns shares in publicly traded companies worth $77bn. These holdings include approximately 13 per cent of American Express, 8.8 per cent of Coca-Cola, 2.6 per cent of Proctor & Gamble, 4.5 per cent of Kraft, 3.6 per cent of Tesco, 1.1 per cent of Wal-Mart, 18 per cent of the Washington Post and 7.6 per cent of Wells Fargo. Some investors have been with Buffett long before he took control of Berkshire. An investor who placed $100 in one of his investment part-nerships in the late 1950s, and placed it in Berkshire after the partnership was dissolved, would find that investment worth more than $14m today. In the 13 years of the partnership funds (1957–69) investors made annual returns greater than that on Berkshire, at almost 30 per cent per year. The funds managed by the young Buffett outperformed the Dow Jones Industrial Average in every year and made money even when the market was sharply down. If you put the two phases of his career – first the partnership, then Berkshire – together then you have a quite remarkable per-formance record, one that, to my knowledge, has not been beaten. Buffett is one of the three richest people in the world. Imagine being one of the lucky people to have trusted Buffett in the early days. It is what investors’ dreams are made of. Apparently, the fol-lowing conversation between two Berkshire shareholders was overheard at the annual meeting in 1996: ‘What price did you buy at?’ The reply: ‘Nineteen,’ says the first. ‘You mean nineteen hun-dred?’ ‘No, nineteen.’25 These shares are now worth over $100,000 each! Warren Buffett said:

I’m convinced that there is much inefficiency in the market . . . When the price of a stock can be influ-enced by a ‘herd’ on Wall Street with prices set at the margin by the most emotional person, or the greediest person, or the most depressed person, it is hard to argue that the market always prices ration-ally. In fact, market prices are frequently nonsensical . . . There seems to be some perverse human characteristic that likes to make easy things difficult. The academic world, if anything, has actually backed away from the teaching of value investing over the last 30 years. It’s likely to continue that way. Ships will sail around the world but the Flat Earth Society will flourish.26

The question is: are these performances possible through chance? Just to muddy the waters, con-sider the following situation. You give dice to 100 million investors and ask them each to throw nine sixes in a row. Naturally most will fail, but some will succeed. You follow up the exercise with a series of interviews to find out how the masters of the die did it. Some say it was the lucky cup they use, others point to astrological charts. Of course we all know that it was purely chance that produced success but try telling that to the gurus and their disciples.


24 He is gradually reducing his holding to give away his fortune mostly to his friends’ Bill & Melinda Gates’ charity, assisting developing countries, particularly with medical aid.

25 Urry (1996), p. 1.26 Warren Buffett (1984).

Warren Buffett has countered this argument – see Exhibit 13.19. It is very difficult to prove either way whether excellent stock picking performance is due to superior analysis in an ineffi-cient environment or merely good fortune. Ultimately you have to make a subjective judgement given the weight of evidence.


Annual percentage change

In per-share book In S&P 500 with Relative results value of Berkshire dividends included (1) – (2)Year (1) (2)

1965 23.8 10.0 13.81966 20.3 (11.7) 32.01967 11.0 30.9 (19.9)1968 19.0 11.0 8.01969 16.2 (8.4) 24.61970 12.0 3.9 8.11971 16.4 14.6 1.81972 21.7 18.9 2.81973 4.7 (14.8) 19.51974 5.5 (26.4) 31.91975 21.9 37.2 (15.3)1976 59.3 23.6 35.71977 31.9 (7.4) 39.31978 24.0 6.4 17.61979 35.7 18.2 17.51980 19.3 32.3 (13.0)1981 31.4 (5.0) 36.41982 40.0 21.4 18.61983 32.3 22.4 9.91984 13.6 6.1 7.51985 48.2 31.6 16.61986 26.1 18.6 7.51987 19.5 5.1 14.41988 20.1 16.6 3.51989 44.4 31.7 12.71990 7.4 (3.1) 10.51991 39.6 30.5 9.11992 20.3 7.6 12.71993 14.3 10.1 4.21994 13.9 1.3 12.61995 43.1 37.6 5.51996 31.8 23.0 8.81997 34.1 33.4 0.71998 48.3 28.6 19.71999 0.5 21.0 (20.5)2000 6.5 (9.1) 15.62001 (6.2) (11.9) 5.72002 10.0 (22.1) 32.12003 21.0 28.7 (7.7)2004 10.5 10.9 (0.4)2005 6.4 4.9 1.52006 18.4 15.8 2.62007 11.0 5.5 5.52008 (9.6) (37.0) 27.42009 19.8 26.5 (6.7)2010 13.0 15.1 (2.1)2011 4.6 2.1 2.5 ––––– ––––– –––––Average annual gain 19.8% 9.2% 10.6%1965–2011

Source: from Berkshire Hathaway, Annual Report, 2011 (www.berkshirehathaway.com). The material is copyrighted and used with permission of the author.

Exhibit 13.18 Berkshire Hathaway’s corporate performance vs. the S&P 500


The superinvestors of Graham-and-Doddsvilleby Warren E. Buffett

Many of the professors who write textbooks . . . argue that the stock market is efficient: that is, that stock prices reflect everything that is known about a company’s prospects and about the state of the economy. There are no undervalued stocks, these theorists argue, because there are smart security analysts who utilize all available information to ensure unfailingly appropriate prices. Investors who seem to beat the market year after year are just lucky. ‘If prices fully reflect available information, this sort of investment adeptness is ruled out,’ writes one of today’s textbook authors. Well, maybe, but I want to present to you a group of investors who have, year in and year out, beaten the Standard & Poor’s 500 stock index. The hypothesis that they do this by pure chance is at least worth examining. Crucial to this examination is the fact that these winners were all well known to me and pre-identified as superior investors, the most recent identification occurring over fifteen years ago. Absent this condition – that is, if I had just recently searched among thousands of records to select a few names for you this morning – I would advise you to stop reading right here. I should add that all these records have been audited. And I should further add that I have known many of those who have invested with these managers, and the checks received by those participants over the years have matched the stated records. Before we begin this examination, I would like you to imagine a national coin-flipping contest. Let’s assume we get 225 million Americans up tomorrow morning and we ask them all to wager a dollar. They go out in the morning at sunrise, and they all call the flip of a coin. If they call correctly, they win a dollar from those who called wrong. Each day the losers drop out, and on the subsequent day the stakes build as all previous winnings are put on the line. After ten flips on ten mornings, there will be approximately 220,000 people in the United States who have correctly called ten flips in a row. They each will have won a little over $1,000. Now this group will probably start getting a little puffed up about this, human nature being what it is. They may try to be modest, but at cocktail parties they will occasionally admit to attractive members of the opposite sex what their technique is, and what marvellous insights they bring to the field of flipping. Assuming that the winners are getting the appropriate rewards from the losers, in another ten days we will have 215 people who have successfully called their coin flips 20 times in a row and who each, by this exercise, have turned one dollar into a little over $1 million. $225 million would have been lost; $225 million would have been won. By then, this group will really lose their heads. They will probably write books on ‘How I Turned a Dollar into a Million in Twenty Days Working Thirty Seconds a Morning’. Worse yet, they’ll probably start jetting around the country attending seminars on efficient coin-flipping and tackling sceptical professors with, ‘If it can’t be done, why are there 215 of us?’ But then some business school professor will probably be rude enough to bring up the fact that if 225 million oran-gutans had engaged in a similar exercise, the results would be much the same – 215 egotistical orangutans with 20 straight winning flips. I would argue, however, that there are some important differences in the examples I am going to present. For one thing, if (a) you had taken 225 million orangutans distributed roughly as the U.S. population is; if (b) 215 winners were left after 20 days; and if (c) you found that 40 came from a particular zoo in Omaha, you would be pretty sure you were on to something. So you would probably go out and ask the zookeeper about what he’s feeding them, whether they had special exercises, what books they read, and who knows what else. That is, if you found any really extraordinary concentrations of success, you might want to see if you could identify concentrations of unu-sual characteristics that might be causal factors. Scientific inquiry naturally follows such a pattern. If you were trying to analyse possible causes of a rare type of cancer – with, say, 1,500 cases a year in the United States – and you found that 400 of them occurred in some little mining town in Montana, you would get very interested in the water there, or the occupation of those afflicted, or other variables. You know that it’s not random chance that 400 come from a small area. You would not necessarily know the causal factors, but you would know where to search. I submit to you that there are ways of defining an origin other than geography. In addition to geographical ori-gins, there can be what I call an intellectual origin. I think you will find that a disproportionate number of successful coin-flippers in the investment world came from a very small intellectual village that could be called Graham-and-Doddsville. A concentration of winners that simply cannot be explained by chance can be traced to this particular intellectual village. Conditions could exist that would make even that concentration unimportant. Perhaps 100 people were simply imitating the coin-flipping call of some terribly persuasive personality. When he called heads, 100 followers

Exhibit 13.19

�


automatically called that coin the same way. If the leader was part of the 215 left at the end, the fact that 100 came from the same intellectual origin would mean nothing. You would simply be identifying one case as a hundred cases. Similarly, let’s assume that you lived in a strongly patriarchal society and every family in the United States conven-iently consisted of ten members. Further assume that the patriarchal culture was so strong that, when the 225 million people went out the first day, every member of the family identified with the father’s call. Now, at the end of the 20-day period, you would have 215 winners, and you would find that they came from only 21.5 families. Some naive types might say that this indicates an enormous hereditary factor as an explanation of successful coin flipping. But, of course, it would have no significance at all because it would simply mean that you didn’t have 215 individual win-ners, but rather 21.5 randomly distributed families who were winners. In this group of successful investors that I want to consider, there has been a common intellectual patriarch, Ben Graham. But the children who left the house of this intellectual patriarch have called their ‘flips’ in very different ways. They have gone to different places and bought and sold different stocks and companies, yet they have had a combined record that simply can’t be explained by random chance. It certainly cannot be explained by the fact that they are all calling flips identically because a leader is signalling the calls to make. The patriarch has merely set forth the intellectual theory for making coin-calling decisions, but each student has decided on his own manner of apply-ing the theory. The common intellectual theme of the investors from Graham-and-Doddsville is this: they search for discrep-ancies between the value of a business and the price of small pieces of that business in the market. Essentially, they exploit those discrepancies without the efficient market theorist’s concern as to whether the stocks are bought on Monday or Thursday, or whether it is January or July, etc. Incidentally, when businessmen buy businesses – which is just what our Graham & Dodd investors are doing through the medium of marketable stocks – I doubt that many are cranking into their purchase decision the day of the week or the month in which the transaction is going to occur. If it doesn’t make any difference whether all of a business is being bought on a Monday or a Friday, I am baffled why academicians invest extensive time and effort to see whether it makes a difference when buying small pieces of those same businesses. Our Graham & Dodd investors, needless to say, do not discuss beta, the capital asset pricing model, or covariance in returns among securities. These are not subjects of any interest to them. In fact, most of them would have difficulty defining those terms. The investors simply focus on two variables, price and value. . . . I think the group that we have identified by a common intellectual home is worthy of study. Incidentally, despite all the academic studies of the influence of such variables as price, volume, seasonality, capitalization size, etc., upon stock performance, no interest has been evidenced in studying the methods of this unusual concentration of value-oriented winners. I begin this study of results by going back to a group of four of us who worked at Graham-Newman Corporation from 1954 through 1956. There were only four – I have not selected these names from among thousands. I offered to go to work at Graham-Newman for nothing after I took Ben Graham’s class, but he turned me down as over-val-ued. He took this value stuff very seriously! After much pestering he finally hired me. There were three partners and four of us at the ‘peasant’ level. All four left between 1955 and 1957 when the firm was wound up, and it’s possible to trace the record of three. The first example is that of Walter Schloss. Walter never went to college, but took a course from Ben Graham at night at the New York Institute of Finance. Walter left Graham-Newman in 1955 and achieved the record shown here over 28 years. [A compound rate of return of 21.3 per cent compared with a market return of 8.4 per cent from 1956 to 1984.] . . . Walter has diversified enormously, owning well over 100 stocks currently. He knows how to identify securi-ties that sell at considerably less than their value to a private owner. And that’s all he does . . . He simply says, if a business is worth a dollar and I can buy it for 40 cents, something good may happen to me, and he does it over and over and over again. He owns many more stocks than I do – and is far less interested in the underlying nature of the business; I don’t seem to have very much influence on Walter. That’s one of his strengths; no one has much influ-ence on him. The second case is Tom Knapp, who also worked at Graham-Newman with me. Tom was a chemistry major at Princeton before the war; when he came back from the war, he was a beach bum. And then one day he read that Dave Dodd was giving a night course in investments at Columbia. Tom took it on a noncredit basis, and he got so interested in the subject from taking that course that he came up and enrolled at Columbia Business School, where he got the MBA degree. He took Dodd’s course again, and took Ben Graham’s course. Incidentally, 35 years later I called Tom to ascertain some of the facts involved here and I found him on the beach again. The only difference is that now he owns the beach! In 1968 Tom Knapp and Ed Anderson, also a Graham disciple, along with one or two other fellows of similar persuasion, formed Tweedy, Browne Partners, and their investment results appear in Table 2 [showing an annual compound rate of return of 20 per cent compared with the market’s return of 7 per cent, 1968–83]. Tweedy,


Browne built that record with very wide diversification. They occasionally bought control of businesses, but the record of the passive investments is equal to the record of the control investments. Table 3 describes the third member of the group who formed the Buffett Partnership in 1957. [Table 3, not reproduced here, shows a compound rate of 29.5 per cent against the market rate of 7.4 per cent, 1957–69.] The best thing he did was to quit in 1969. Since then, in a sense, Berkshire Hathaway has been a continuation of the partnership in some respects. There is no single index I can give you that I would feel would be a fair test of invest-ment management at Berkshire. But I think that any way you figure it, it has been satisfactory. Table 4 shows the record of the Sequoia Fund, which is managed by a man whom I met in 1951 in Ben Graham’s class, Bill Ruane. [Table 4, not reproduced here, shows compound annual return of 17.2 per cent against a market return of 10 per cent, 1970–84.] After getting out of Harvard Business School, he went to Wall Street. Then he realized that he needed to get a real business education so he came up to take Ben’s course at Columbia, where we met in early 1951. Bill’s record from 1951 to 1970, working with relatively small sums, was far better than average. When I wound up Buffett Partnership I asked Bill if he would set up a fund to handle all our partners, so he set up the Sequoia Fund. He set it up at a terrible time, just when I was quitting. He went right into the two-tier market and all the difficulties that made for comparative performance for value-oriented investors. I am happy to say that my partners, to an amazing degree, not only stayed with him but added money, with the happy result shown. There’s no hindsight involved here. Bill was the only person I recommended to my partners, and I said at the time that if he achieved a four-point-per-annum advantage over the Standard & Poor’s, that would be solid per-formance. Bill has achieved well over that, working with progressively larger sums of money. . . . I should add that in the records we’ve looked at so far, throughout this whole period there was practically no duplication in these portfolios. These are men who select securities based on discrepancies between price and value, but they make their selections very differently . . . The overlap among these portfolios has been very, very low. These records do not reflect one guy calling the flip and fifty people yelling out the same thing after him. . . . A friend of mine who is a Harvard Law graduate . . . set up a major law firm. I ran into him in about 1960 and told him that law was fine as a hobby but he could do better. He set up a partnership quite the opposite of Walter’s. His portfolio was concentrated in very few securities and therefore his record was much more volatile but it was based on the same discount-from-value approach [average annual compound rate of return of 19.8 per cent compared with market return of 5 per cent p.a., 1962–75]. He was willing to accept greater peaks and valleys of performance, and he happens to be a fellow whose whole psyche goes toward concentration, with the results shown [not reproduced]. Incidentally, this record belongs to Charlie Munger, my partner for a long time in the operation of Berkshire Hathaway. When he ran his partnership, however, his portfolio holdings were almost completely dif-ferent from mine and the other fellows mentioned earlier. Table 6 [not reproduced here] is the record of a fellow who was a pal of Charlie Munger’s, another non-business school type – who was a math major at USC. He went to work for IBM after graduation and was an IBM salesman for a while. After I got to Charlie, Charlie got to him. This happens to be the record of Rick Guerin. Rick, from 1965 to 1983, against a compounded gain of 316 percent for the S&P, came off with 22,200 percent which, probably because he lacks a business school education, he regards as statistically significant. One sidelight here: it is extraordinary to me that the idea of buying dollar bills for 40 cents takes immediately with people or it doesn’t take at all. It’s like an inoculation. If it doesn’t grab a person right away, I find that you can talk to him for years and show him records, and it doesn’t make any difference. They just don’t seem able to grasp the concept, simple as it is. A fellow like Rick Guerin, who had no formal education in business, understands imme-diately the value approach to investing and he’s applying it five minutes later. I’ve never seen anyone who became a gradual convert over a ten-year period to this approach. It doesn’t seem to be a matter of IQ or academic training. It’s instant recognition, or it is nothing. . . . Stan Perlmeter . . . was a liberal arts major at the University of Michigan who was a partner in the advertising agency of Bozell & Jacobs. We happened to be in the same building in Omaha. In 1965 he figured out I had a better business than he did, so he left advertising. Again, it took five minutes for Stan to embrace the value approach. [Performance: 23 per cent p.a. compared with market return of 7 per cent 1966–83.] Perlmeter does not own what Walter Schloss owns. He does not own what Bill Ruane owns. These are records made independently. But every time Perlmeter buys a stock it’s because he’s getting more for his money than he’s paying. That’s the only thing he’s thinking about. He’s not looking at quarterly earnings projections, he’s not look-ing at next year’s earnings, he’s not thinking about what day of the week it is, he doesn’t care what investment research from any place says, he’s not interested in price momentum, volume, or anything. He’s simply asking: What is the business worth? . . . So these are . . . records of ‘coin-flippers’ from Graham-and-Doddsville. I haven’t selected them with hind-sight from among thousands. It’s not like I am reciting to you the names of a bunch of lottery winners – people I had never heard of before they won the lottery. I selected these men years ago based upon their framework for �

Eugene Fama is perhaps the most well-known advocate of EMH, with a string of quantitative empirical papers to his name. And yet even he, in this 2002 newspaper interview (Exhibit 13.20) shows some doubts.


investment decision-making. I knew what they had been taught and additionally I had some personal knowledge of their intellect, character, and temperament. It’s very important to understand that this group has assumed far less risk than average; note their record in years when the general market was weak. While they differ greatly in style, these investors are, mentally, always buying the business, not buying the stock. A few of them sometimes buy whole businesses. Far more often they simply buy small pieces of businesses. Their attitude, whether buying all or a tiny piece of a business, is the same. Some of them hold portfolios with dozens of stocks; others concentrate on a hand-ful. But all exploit the difference between the market price of a business and its intrinsic value. I’m convinced that there is much inefficiency in the market. These Graham-and-Doddsville investors have suc-cessfully exploited gaps between price and value. . . . In conclusion, some of the more commercially minded among you may wonder why I am writing this article. Adding many converts to the value approach will perforce narrow the spreads between price and value, I can only tell you that the secret has been out for 50 years, ever since Ben Graham and Dave Dodd wrote Security Analysis, yet I have seen no trend toward value investing in the 35 years that I’ve practiced it . . . There will continue to be wide discrepancies between price and value in the marketplace, and those who read their Graham and Dodd will continue to prosper.

Source: Warren Buffett (1984), an edited transcript of a talk given at Columbia University in 1984. Reproduced in Hermes, magazine of Columbia Business School, Fall 1984 and in both the 1997 and the 2003 reprints of Graham (1973).

Exhibit 13.20

One of the biggest unanswered questions in financial economics is why Eugene Fama has yet to win the Nobel Prize. The University of Chicago business school professor coined the term ‘efficient markets’ with his 1963 doctoral thesis, pioneered empirical research into the behaviour of capital markets and, in the early 1990s, devised the ‘three-factor model’ that led to a whole new taxonomy of investment funds.

The passive fund management industry owes its rise to the insight that markets quickly and accurately assimilate new information, and cannot be beaten over the long term without the assumption of additional risk.

So, 40 years on, does Prof Fama believe more or less strongly in the efficiency of capital markets?

‘I’ve never said that markets are totally efficient. I’ve always said that for most investors, most of the time, markets are efficient. For most corporate managers, markets are efficient – for all practical purposes.’

How, then, does he explain the extraordinary record of Warren Buffet, who has beaten the market with remarkable consistency? ‘I think Warren Buffet is great. I am willing to believe that he wins, OK. But what he says is that he can pick an [undervalued] company once every couple of years. And he is the best. He says that for everything else, markets are efficient.

‘Remember also that he doesn’t just pick companies, he runs them. That is a different activity. No one would argue against the idea that, if you participate in these companies, you might be able to make them better or worse. It doesn’t mean that the companies were inefficiently priced to begin with, just that something can be done to make them more attractive. If Warren Buffet can do it only once every couple of years, that is the best thing you can say about market efficiency.’

What about stock market bubbles and crashes? Surely, stocks cannot have been correctly priced on Monday morning if they are worth 30 per cent

Forty years on, Fama holds to his big ideaThe Chicago based professor says his theory has stood the test of timesays Simon London

Strong-form tests

It is well known that it is possible to trade shares on the basis of information not in the public domain and thereby make abnormal profits. The mining engineer who discovers a rich seam of silver may buy the company shares before the market is told of the likely boost to profits. The direc-tor who becomes aware of lost orders and declining competitive position may quietly sell shares to ‘diversify his interests’ or ‘pay for school fees’, you understand. The merchant banker who hears of a colleague assisting one firm to plan a surprise takeover bid for another has been known to purchase shares (or options) in the target firm. Stock markets are not strong-form efficient. Trading on inside knowledge is thought to be a ‘bad thing’. It makes those outside of the charmed circle feel cheated. A breakdown of the fair game perception will leave some investors feeling that the inside traders are making profits at their expense. If they start to believe that the market is less than a fair game they will be more reluctant to invest and society will suffer. To avoid the loss of confidence in the market most stock exchanges attempt to curb insider dealing. It was made a criminal offence in the UK in 1980 where insider dealing is considered to be, besides dealing for oneself, either counselling or procuring another individual to deal in the securities or communicating knowledge to any other person, while being aware that he or she (or someone else) will deal in those securities. The term ‘insider’ now covers anyone with sensitive information, not just a company director or employee. Most modern economies have rules on insider dealing



less by the end of the day? ‘I don’t know why these things happen so quickly’, he concedes. ‘We don’t know enough about the way in which information gets assimilated into prices. But if you look at crashes, half of them turn out to be too small and half turn out to be too big.

‘The 1987 crash was clearly too big because the market came back so quickly, the ’29 crash was too small because the market carried on going down. They are both mistakes, but they are unbiased mistakes.’

In other words, stock market crashes don’t have any statistical significance and there is no pattern that can be used as evidence against efficient markets.

Prof Fama’s belief in market efficiency has been strengthened by the many inconclusive studies over the years attempting to disprove it: ‘Today there are 10,000 finance academics looking for violations of one theory or another, including efficient markets. I think it has stood up very well.’

The fund management industry has a vested interest in undermining the idea that markets are efficient. Fees charged by active managers only make sense if investors believe they have good chance of beating the market indices.

The snag for active managers is that studies have failed to find real evidence of ‘persistency’ in the performance of managers who invest on the basis of either fundamental analysis of companies or

technical analysis of market trends. ‘The thing people can’t deal with is that, if you look at the performance of active managers, there is nothing there,’ says Prof Fama. According to efficient markets theory, the only way to beat, the market over the long term is to accept additional risk. Prof Fama’s ‘three-factor model,’ published with Ken French of Dartmouth College in 1993, says the returns of any portfolio are determined by:

� The market risk of investing in equities rather than less volatile vehicles;

� Whether the stocks in the portfolio have growth or value characteristics;

� Whether the stocks are issued by large medium or small-cap companies.

According to the model, investors in small-cap value stocks should achieve the highest returns over the long term because they are adopting the most risk. But Prof Fama admits that the nature of these risks remains little understood.

‘We don’t claim that we totally understand what additional risks you are taking, particularly when it comes to size. But do you think that small firms have the same cost of capital as big firms? No, they have a higher cost of capital. This is the flip side of expected returns: We don’t fully understand these mechanisms but we have observed the effect.’

Source: Financial Times, 3 June 2002, p. 4. Reprinted with permission.

Financial Times, 3 June 2002, p. 4.All Rights Reserved.

and the EU has a directive on the subject. Despite the complex legislation and codes of conduct it is hard to believe that insider trading has been reduced significantly in the last two decades. It would appear that the lawyers have great difficulty obtaining successful prosecutions. Since 2001 the Financial Services Authority has had the power to fine insiders for ‘market abuse’ encompassing both insider dealing and attempts to manipulate the market, for example through misleading statements. This is under civil law rather than criminal law and therefore has a lower burden of proof. Another weapon in the fight against insiders is to raise the level of information disclosure: making companies release price-sensitive information quickly. The London Stock Exchange and the United Kingdom Listing Authority have strict guidelines to encourage companies to make announcements to the market as a whole as early as possible, on such matters as current trading conditions and profit warnings. A third approach is to completely prohibit certain individuals from dealing in the compa-ny’s shares for crucial time periods. For example, directors of quoted firms are prevented by the ‘Model Code for Director Dealings’ from trading shares for a minimum period (two months) before an announcement of regularly recurring information such as annual results. The Code also precludes dealing before the announcement of matters of an exceptional nature involving unpub-lished information which is potentially price sensitive. These rules apply to other employees in possession of price-sensitive information. There is a grey area which stands between trading on inside knowledge and trading purely on publicly available information. Some investment analysts, though strictly outsiders, become so knowledgeable about a firm that they have some degree of superior information. Their judgement or guesstimates about future prospects are of a higher order than those of other analysts and cer-tainly beyond anything the average shareholder is capable of. They may make regular visits to the company head office and operating units. They may discuss the opportunities and potential prob-lems for the firm and the industry with the directors and with competitors’ employees. Despite the strict rules concerning directors briefing one analyst better than the generality of shareholders it may be possible to ‘read between the lines’ and gather hints to give an informed edge. The hypothesis that there are some exceptional analysts has limited empirical backing and relies largely on anecdotal evidence and so this point should not be overemphasised. It is clear from previous sections of this chapter that the vast majority of professional analysts are unable to outperform the market. John Kay, the respected economist, believes that economists need to be much more humble about proclaiming the validity of the EMH – see Exhibit 13.21.


Exhibit 13.21

Warren Buffett said most of what you need to know about efficient markets. ‘Observing correctly that the market was frequently efficient, they [academics, investment professionals and corporate managers] went on to conclude incorrectly that it was always efficient. The difference between the propositions is night and day.’

The difference between these propositions is also the difference between a $50bn fortune and the returns of the average investor. Mr Buffett has made his money not from the part that is frequently efficient, but from the part that is infrequently inefficient.

The efficient market hypothesis has been the bedrock of financial economics for almost 50 years. One of the

architects of the theory, Michael Jensen, famously remarked that ‘there is no other proposition in economics which has more solid empirical evidence supporting it’. Along with other writers of the time – such as Burton Malkiel, whose A Random Walk down Wall Street is now in its ninth edition – Prof Jensen was anxious to dispel the mystique of the financial services industry. Prices followed a random walk, so paying for active management was a waste of money.

Market efficiency is a hypothesis about the way markets react to information and does not necessarily imply that markets promote economic efficiency in a wider sense. But there

Markets after the age of efficiencyBy John Kay

Behavioural finance

There has been a forceful attack on the EMH by finance specialists drawing on a combination of human behavioural literature and their knowledge of markets. The EMH rests on the assump-tion that all investors are rational, or, even if there are some irrational investors, that the actions of rational informed investors will eliminate pricing anomalies through arbitrage. The behavioural finance proponents argue that investors frequently make systematic errors and these errors can push the prices of shares (and other financial securities) away from fundamental value for considerable periods of time. This is a field of intellectual endeavour that is attracting increasing numbers of adherents as the evidence on apparent inefficiencies grows. Behavioural finance models offer plausible reasoning for the phenomena we observe in the pattern of share prices. They offer persuasive explanations for the outperformance of low PER, high dividend yield and low book-to-market ratio shares as



is a relationship between the two concepts of efficiency. It has long suited market practitioners and rightwing ideologues to encourage such confusion. Since markets are efficient, they argue, interference in markets is counter-productive and more markets mean more efficiency.

As anyone who has taken Finance 101 knows, there are three versions of the efficient market hypothesis. The strong version claims that everything you might know about the value of securities is ‘in the price’. It is closely bound up with the idea of rational expectations, whose implications have dominated macroeconomics for 30 years. Policy interventions are mostly futile, monetary policy should follow simple rigid rules, market prices are a considered reflection of fundamental values and there can be no such things as asset-price bubbles.

These claims are not just empirically false but contain inherent contradictions. If prices reflect all available information, why would anyone trouble to obtain the information they reflect? If markets are informationally efficient, why is there so much trade between people who take different views of the same future? If the theory were true, the activities it purports to explain would barely exist.

Yet although efficient market theory is not true, it may nevertheless be illuminating. The absurdities of rational expectations come from the physics envy of many economists, who mistake occasional insights for universal truths. Economic models are illustrations and metaphors, and cannot be comprehensive descriptions even of the part of the world they describe. There is plenty to be learnt from the theory if you do not take it too seriously – and, like Mr Buffett, focus on the infrequent inefficiency rather than the frequent efficiency.

The weak efficient market theory tells us that past prices are no guide to what will happen to security prices in future. There is a good deal of evidence for this claim: you would be as well employed studying the patterns on your palm as patterns on charts. But there is also evidence of a tendency for short-term price movements to continue in the same direction – momentum is real. If you know precisely when the short term becomes the long term, this would make you very rich. It is possible to make money – or policy – through reading boom-and-bust cycles. But most participants do not.

The semi-strong version of the theory claims that markets reflect all publicly available information about securities. What is general knowledge will be in the price, so information such as ‘General Electric is a well-managed company’ or ‘Britain has a large budget deficit’, although correct, is useless to investors. But inside information, or original analysis, might add value.

The strong version of the efficient market hypothesis is popular because the world it describes is free of extraneous social, political and cultural influences. Yet if reality were shaped by beliefs about the world, not only would we need to investigate how beliefs are formed and influenced – something economists do not want to do – but models and predictions would be contingent on these beliefs. Of course, models and predictions are so contingent, and an understanding of how beliefs form is indispensable. Economics is not so much the queen of the social sciences but the servant, and needs to base itself on anthropology, psychology – and the sociology of ideologies. The future of investing – and economics – lies in that more eclectic vision.

Financial Times, 7 October 2009, p. 17.All Rights Reserved.

well as the poor performance of ‘glamour’ shares. They can also be drawn on to shed light on both return reversal and momentum effects. In addition, behavioural science has a lot to offer when it comes to understanding stock market bubbles and irrational pessimism. Many of the investors who made a fortune in the twentieth century have been saying all along that to understand the market you must understand the psychology of investors. In the 1960s, 1970s and even the 1980s, they were denounced as naive at best by the dyed-in-the-wool quantitative financial economist – the economists had ‘scientific proof’ of the market’s efficiency. They insisted that even if investors were generally irrational the market had inherent mechanisms to arrive at the efficient price, leaving no abnormal returns to be had. The successful investors were merely lucky. Worse! They were lucky and had the nerve to go against the scien-tific ‘evidence’ and publicly declare that they believed that there are sound investment principles which permit outperformance. The successful investors continued to believe in the irrationality and exploitability of markets despite the onslaught from many university economists who were characterised as believing that ‘It might work in practice, but it’ll never work in theory’. Eventually a growing band of respected academics provided theoretical and empirical backing to the behavioural view of financial mar-kets. Now the debate has reached a fascinating point with high-quality modelling and empirical evidence on both sides.

The three lines of defence for EMHTo defend the EMH its adherents have three progressively stronger arguments which have to be surmounted if the behavioural finance advocates are to be able to attack the core.27

1 Investors are rational and hence value securities rationally.

2 Even if some investors are not rational, their irrationally inspired trades of securities are random and therefore the effects of their irrational actions cancel each other out without moving prices away from their efficient level.

3 If the majority of investors are irrational in similar ways and therefore have a tendency to push security values away from the efficient level this will be countered by rational arbitrageurs who eliminate the influence of the irrational traders on prices.

Under the first condition all investors examine securities for their fundamental value. That is, they calculate the present value of the future income flow associated with the security using an appropriate discount rate given the risk level (see Chapter 17). If any new information comes along which will increase future flows or decrease the discount rate then the price will rise to the new efficient level instantly. Likewise, bad news results in a lower efficient price. This barrier is easy to attack and demolish. It is plain from anecdotal evidence and from empirical study that the majority of share traders do not assess fundamental value – just ask those who are day-traders or those who buy on the basis of a tip from a friend, a newspaper or broker. The second barrier is more of a challenge. It accepts individual irrational behaviour but the result is collective rationality in pricing because the irrational trades are evenly balanced and so the effect is benign. This may explain the large volume of trades as irrational investors exchange securities with each other, but this does not lead to systematic inefficient pricing away from fundamental value. The key assumption to be attacked here is the absence of correlation in the actions of irrational investors. There is growing evidence that investors do not deviate from rationality randomly but there is a bias to deviate in the same way (that is, there is positive correla-tion between their deviations) and therefore they lead prices away from fundamental value. The next section of this chapter provides an outline of some of the psychological biases that are being studied to try to explain apparent inefficiencies in pricing. The third argument says that the actions of rational arbitrageurs are strong enough to restore efficiency even in the presence of numerous investors making cognitive errors. Arbitrage is the act of exploiting price differences on the same security or similar securities by simultaneously selling the overpriced security and buying the underpriced security. If a security did become overpriced


27 These three arguments are identified by Andrei Shleifer in his excellent book Inefficient Markets (2000).

because of the combined actions of irrational investors, smart investors would sell this security (or if they did not own it, ‘sell it short’) and simultaneously purchase other ‘similar securities’ to hedge their risks. In a perfect arbitrage they can make a profit without any risk at all (and even without money). The arbitrageurs’ selling action brings down the security’s price to its fundamen-tal value in the EMH. If a security became underpriced arbitrageurs would buy the security and, to hedge risk, would sell short essentially similar securities, lifting the price of the security to its efficient level. The arbitrage argument is impressive and forms a strong bulwark against the financial behav-iourists. However, there are some weaknesses. Shleifer (2000) points out a number of reasons why arbitrage does not work well in the real world and therefore prices are not returned to funda-mental value. To be effective the arbitrageur needs to be able to purchase or sell a close-substitute security. Some securities, e.g. futures and options, usually have close substitutes, but in many instances there is no close substitute and so locking in a safe profit is not possible. For example, imagine that you, as a rational investor, discover that Unilever’s shares are undervalued. What other security (securities) would you sell at the same time as you purchase Unilever’s shares to obtain a risk-free return when the price anomaly is detected? If we were talking about the price of a tonne of wheat of the same quality selling on two different markets at different prices we could buy in the low-price market and simultaneously sell in the high-price market and make a profit (guaranteed without risk) even if the price difference was only 10p. But what can you use in arbi-trage trade that is the same as a Unilever share? Well, you might consider that Procter & Gamble shares are close enough and so you sell these short.28 You expect that in six months the pricing anomaly will correct itself and you can close your position in Unilever by selling and close your position in P&G by buying its shares. But this strategy is far from the risk-free arbitrage of econo-mists’ ideal. You face the risk of other fundamental factors influencing the shares of Unilever and P&G (e.g. a strike, a product flop). You also face the risk that the irrational investors push irra-tionality to new heights. That is, the price does not gradually move towards the fundamental value over the next six months, but away from it. If this happens you lose money as a buyer of Unilever shares and have no offsetting gain on P&G shares. There is growing evidence of the problem of continued movement away from the fundamental value even after an anomaly has been spotted by arbitrageurs (e.g. Froot and Dabora (1999)). For anecdotal evidence we need only remem-ber back to 1999 and the pricing of dotcom stocks where arbitrageurs sold at high prices only to see the price climb higher as thousands of ill-informed investors piled in. This type of risk facing the arbitrageur is called ‘noise trader risk’ (De Long et al. (1990)) because it is the actions of the poorly informed investors that create noise in the price series; and this can get worse rather than better. So, in the real world ‘with a finite risk-bearing capacity of arbitrageurs as a group, their aggregate ability to bring prices of broad groups of securities into line is limited’ (Shleifer (2000), p. 14). Trading in overvalued or undervalued shares and using imperfect substitutes to offset a posi-tion is termed ‘risk arbitrage’ and is a completely different kettle of fish from risk-free arbitrage.29 Risk arbitrage entails a calculation of the statistical likelihood of the convergence of relative prices and does not deal with certainties.


28 We are assuming that selling shares that you do not own (and therefore have to borrow) is easy and available to a large number of potential investors. However, in reality, borrowing shares is costly, often impossible and open to only a few institutional investors. Also your period of going short is usually only for days, weeks or a few months rather than years.

29 Arbitrageurs face a number of risks. a Fundamental risk: they may be wrong about perceived under- or overpricing.b Noise trader risk: i Horizon risk: prices may revert to a correct level eventually, but the length of time

needed may reduce the arbitrageurs’ return to very little, e.g. if a 5 per cent underpricing is corrected in one month the annual rate of return is 79.6 per cent. If it takes two years the annual rate of return is under 2.5 per cent. ii Margin risk: arbitrageurs often borrow to buy into positions. If the market moves against them the lender may ask for more collateral or, in the derivatives market may ask for more margin (see Chapter 21). Arbitrageurs may not be able to meet these requirements and so may be forced to sell their positions at inconvenient times. iii Short covering risk: if the arbitrageur has bor-rowed shares to go short the lender may not be able to continue to supply shares for more than a few days, forcing the arbitrageur to liquidate the position prematurely.

Shleifer builds a behaviourally based model on the foundation of two observations of real-world markets.

1 Many securities do not have perfect, or even good, substitutes, making arbitrage risky.

2 Even if a good substitute is available arbitrage remains risky because of noise trader risk, and the possibility that prices will not converge to fundamental values quickly enough to suit the arbitrageur’s time horizon.

He concludes that market efficiency will only be an extreme special case and financial markets in most scenarios are not expected to be efficient.

Some cognitive errors made by investorsInvestors are subject to a variety of psychological tendencies that do not fit with the economists’ ‘rational man’ model. This, it is argued, can lead to markets being heavily influenced by inves-tor sentiment. The combination of limited arbitrage and investor sentiment pushing the market leads to inefficient pricing. Both elements are necessary. If arbitrage is unlimited then arbitrageurs will offset the herd actions of irrational investors so prices quickly and correctly move to incorpo-rate relevant news. In the absence of investor sentiment prices would not move from fundamental value in the first place. Listed below are some of the psychological tendencies that are thought to impact on investors’ buying and selling decisions and thus to create sentiment.

OverconfidenceWhen you ask drivers how good they are relative to other drivers research has shown that 65–80 per cent will answer that they are above average. Investors are as overconfident about their trad-ing abilities as about their driving abilities. People significantly overestimate the accuracy of their forecasts. So, when investors are asked to estimate the profits for a firm one year from now and to express the figures in terms of a range where they are confident that the actual result has a 95 per cent chance of being within the projected range, they give a range that is far too narrow. Investors make bad bets because they are not sufficiently aware of their informational disadvan-tage. This line of research may help explain the underreaction effect (Chui et al. 2010). Investors experience unanticipated surprise at, say, earnings announcements because they are overconfident about their earnings predictions. It takes a while for them to respond to new information in the announcement (due to conservatism – see below) and so prices adjust slowly. This may contribute to price momentum and earnings momentum. Overconfidence may be caused, at least in part, by self-attribution bias. That is, investors ascribe success to their own brilliance, but failures in stock picking to bad luck. Overconfidence may be a cause of excessive trading because investors believe they can pick winners and beat the market (Barber and Odean, 1999, 2000, Puetz and Ruenzi (2011)). Inexperienced investors are, apparently, more confident that they can beat the market than experienced investors.

RepresentativenessRepresentativeness is the making of judgements based on stereotypes. It is the tendency to see identical situations where none exist. For example, if Michael is an extrovert, the life and soul of the party, highly creative and full of energy, people are more likely to judge that he is an advertis-ing executive rather than a postman. Representativeness can be misleading. Michael is more likely to be a postman than an advertising executive, even though he ‘sounds’ to be typical of advertising executives: there are far more postmen than advertising executives. People overweight the repre-sentative description and underweight the statistical base evidence. If there is a sharp decline in the stock market, as in 1987, 2001 or 2008, you will read articles pointing out that this is 1929 all over again. These will be backed up by a chart showing the index movement in 1929 and recent index movements. The similarities can be striking, but this does not mean that the Great Depression is about to be repeated, or even that share prices will fall for the next three years. The similarities between the two situations are superficial. The economic funda-mentals are very different. Investors tend to give too much weight to representative observation (e.g. share price movements) and underweight numerous other factors.


Representativeness may help explain the return reversal effect. People look for patterns. If a share has suffered a series of poor returns investors assume that this pattern is representative for that com-pany and will continue in the future. They forget that their conclusion could be premature and that a company with three bad years can produce several good profit figures. Similarly investors overreact in being too optimistic about shares that have had a lot of recent success. It may also explain why unit trusts and investment trusts with high past performance attract more of investors’ capital even though studies have shown that past performance is a poor predictor of future performance – even poor-quality managers can show high returns purely by chance.

ConservatismInvestors are resistant to changing an opinion, even in the presence of pertinent new information. So, when profits turn out to be unexpectedly high they initially underreact. They do not revise their earnings estimates enough to reflect the new information and so one positive earnings surprise is followed by another positive earnings surprise.

Narrow framingInvestors’ perceptions of risk and return are highly influenced by how the decision problems are framed. Many investors ‘narrow frame’ rather than look at the broader picture. For example, an investor aged under 35 saving for retirement in 30 years pays too much attention to short-term gains and losses on a portfolio. Another investor focuses too much on the price movements of a single share, although it represents only a small proportion of total wealth. This kind of narrow framing can lead to an over-estimation of the risk investors are taking, especially if they are highly risk averse. The more narrow the investor’s focus, the more likely he is to see losses. If the inves-tor took a broad frame he would realise that despite short-term market fluctuations and one or two down years the equity market rises in the long term and by the time of retirement a well-diver-sified portfolio should be worth much more than it is today. Likewise, by viewing the portfolio as a whole the investor does not worry excessively about a few shares that have performed poorly.

Ambiguity aversionPeople are excessively fearful when they feel that they do not have very much information. On the other hand they have an excessive preference for the familiar on which they feel they have good infor-mation: as a result they are more likely to gamble. For example, ambiguity aversion may explain the avoidance of overseas shares despite the evidence of the benefits of international diversification.

Positive feedback and extrapolative expectationsStock market bubbles may be, at least partially, explained by the presence of positive-feedback traders who buy shares after prices have risen and sell after prices fall. They develop extrapolative expectations about prices. That is, simply because prices rose (fell) in the past and a trend has been established investors extrapolate the trend and anticipate greater future price appreciation (falls). This tendency has also been found in house prices and in the foreign exchange markets. George Soros describes in his books (1987, 1998, 2008) his exploitation of this trend-chasing behaviour in a variety of financial and real asset markets. Here the informed trader (e.g. Soros) can buy into the trend thus pushing it along, further away from fundamental values, in the expectation that uninformed investors will pile in and allow the informed trader to get out at a profit. Thus the informed trader creates addi-tional instability instead of returning the security to fundamental value through arbitrage.

RegretExperimental psychologists have observed that people will forgo benefits within reach in order to avoid the small chance of feeling they have failed. They are overly influenced by the fear of feel-ing regret.

Confirmation biasPeople desire to find information that agrees with their existing view. Information that conflicts is ignored. For example, in 2007 many people ignored the arguments suggesting property prices might fall.


Cognitive dissonanceIf a belief has been held for a long time people continue to hold it even when such a belief is plainly contradicted by the evidence. People experience mental conflict when presented with evi-dence that their beliefs or assumptions are wrong, resulting in denial for a considerable period.

Availability biasPeople may focus excessively on a particular fact or event because it is more visible, fresher in the mind or emotionally charged, at the expense of seeing the bigger picture. The bigger picture may incorporate soundly based probabilities. For example, following a major train crash, people tend to avoid train travel and use their cars more. However, the bigger picture based on the statisti-cal evidence reveals that train travel is far safer than road transport. In financial markets, if some particularly high-profile companies in an industrial sector (e.g. IT) have produced poor results, investors might abandon the whole sector, ignoring the possibility that some excellent companies may be selling at low prices. They overweight the prominent news.

Miscalculation of probabilities Experiments have shown that people attach too low a probability to likely outcomes and too high a probability to quite unlikely ones. Can this explain the low valuations of ‘old economy’ shares in the late 1990s as the technological revolution was in full swing? Did investors underestimate these companies’ prospects for survival and their ability to combine the new technology with their tra-ditional strengths? At the same time did investors overestimate the probability of all those dotcom start-ups surviving and becoming dominant in their segments?

AnchoringWhen people are forming quantitative assessments their views are influenced by suggestion. So, for example, people valuing shares are swayed by previous prices. They anchor their changes in valuation on the value as suggested in the past. This may contribute to understanding post- earnings-announcement drift as investors make gradual adjustments to historic figures.

There is some meeting of the ways between the rational and the irrational schools of thought, so that investors are viewed as flawed rationalists rather than hopelessly irrational beings. These quasi-rational humans try hard to be rational but are susceptible to repeating the same old mis-takes. They have memory limitations, cognitive limitations and emotional limitations. William Sharpe, the Nobel laureate and developer of the CAPM, believes there is much to be gained by stepping outside economists’ models and allowing for human behaviour – see Exhibit 13.22.


Exhibit 13.22

Prof Sharpe’s [Bill Sharpe, the brains behind the Capital Asset Pricing Model] analysis of markets and finance has yet to come to rest and he has several answers as to where financial economics is heading. First, he argues that finance has become too obsessed with mathematics.

‘We have got so intent on having elegant solutions to closed-form equations that we have tolerated

some really stupid assumptions about people’s preferences,’ he says.

Linked to this, he wants financial economists to strive for a better understanding of how people really act.

Does that make Prof Sharpe a closet fan of behavioural finance, which tries to explain financial markets by looking at human psychology?

Life at the Sharpe end of economic modellingThe godfather of index funds says psychology will contribute to the next big breakthroughwrites Simon London

Richard Thaler, a leader of the behavioural finance school, discusses the EMH in Exhibit 13.23.



‘I’m a fan of good behavioural finance. It is not a question of trying to show that people are irrational or throwing out all the models that involve rationality. The interesting thing is to find out what kinds of decisions people make under conditions of uncertainty if they know what they are doing.’

It is from this marriage of psychology and economics that Prof Sharpe expects the next breakthrough in finance. Fund managers, watch this space.

Financial Times, 29 July 2002, p. 4.All Rights Reserved.

Exhibit 13.23

I recently had the pleasure of reading Justin Fox’s new book The Myth of the Rational Market. It offers an engaging history of the research that has come to be called the ‘efficient market hypothesis’. It is similar in style to the classic by the late Peter Bernstein, Against the Gods. All the quotes in this column are taken from it. The book was mostly written before the financial crisis. However, it is natural to ask if the experiences over the last year should change our view of the EMH.

It helps to start with a quick review of rational finance. Modern finance began in the 1950s when many of the great economists of the second half of the 20th century began their careers. The previous generation of economists, such as John Maynard Keynes, were less formal in their writing and less tied to rationality as their underlying tool. This is no accident. As economics began to stress mathematical models, economists found that the simplest models to solve were those that assumed everyone in the economy was rational. This is similar to doing physics without bothering with the messy bits caused by friction. Modern finance followed this trend.

From the starting point of rational investors came the idea of the efficient market hypothesis, a theory first elucidated by my colleague and golfing buddy Gene Fama. The EMH has two components that I call ‘The Price is Right’ and ‘No Free Lunch’. The price is right principle says asset prices will, to use Mr Fama’s words, ‘fully reflect’ available information, and thus ‘provide accurate signals for

resource allocation’. The no free lunch principle is that market prices are impossible to predict and so it is hard for any investor to beat the market after taking risk into account.

For many years the EMH was ‘taken as a fact of life’ by economists, as Michael Jensen, a Harvard professor, put it, but the evidence for the price is right component was always hard to assess. Some economists took the fact that prices were unpredictable to infer that prices were in fact ‘right’. However, as early as 1984 Robert Shiller, the economist, correctly and boldly called this ‘one of the most remarkable errors in the history of economic thought’. The reason this is an error is that prices can be unpredictable and still wrong; the difference between the random walk fluctuations of correct asset prices and the unpredictable wanderings of a drunk are not discernable.

Tests of this component of EMH are made difficult by what Mr Fama calls the ‘joint hypothesis problem’. Simply put, it is hard to reject the claim that prices are right unless you have a theory of how prices are supposed to behave. However, the joint hypothesis problem can be avoided in a few special cases. For example, stock market observers – as early as Benjamin Graham in the 1930s – noted the odd fact that the prices of closed-end mutual funds (whose funds are traded on stock exchanges rather than redeemed for cash) are often different from the value of the shares they own. This violates the basic building block of finance – the law of one price – and does not depend on any pricing model.

Markets can be wrong and the price is not always rightBy Richard Thaler

�

Misconceptions about the efficient market hypothesis

There are good grounds for doubting some aspects of the EMH and a reasoned debate can take place with advocates for efficiency and inefficiency stating their cases with rigorous argument and robust empirical methodology. However, the high-quality debate has sometimes been overshad-owed by criticism based on one or more misunderstandings of the EMH. There are three classic misconceptions.



During the technology bubble other violations of this law were observed. When 3Com, the technology company, spun off its Palm unit, only 5 per cent of the Palm shares were sold; the rest went to 3Com shareholders. Each shareholder got 1.5 shares of Palm. It does not take an economist to see that in a rational world the price of 3Com would have to be greater than 1.5 times the share of Palm, but for months this simple bit of arithmetic was violated. The stock market put a negative value on the shares of 3Com, less its interest in Palm. Really.

Compared to the price is right component, the no free lunch aspect of the EMH has fared better. Mr Jensen’s doctoral thesis published in 1968 set the right tone when he found that, as a group, mutual fund managers could not outperform the market. There have been dozens of studies since then, but the basic conclusion is the same. Although there are some anomalies, the market seems hard to beat. That does not prevent people from trying. For years people predicted fees paid to money managers would fall as investors switched to index funds or cheaper passive strategies, but instead assets were directed to hedge funds that charge very high fees.

Now, a year into the crisis, where has it left the advocates of the EMH? First, some good news. If anything, our respect for the no free lunch component should have risen. The reason is related to the joint hypothesis problem. Many investment strategies that seemed to be beating the market were not doing so once the true measure of risk was considered. Even Alan Greenspan, the former Federal Reserve chairman, has admitted that investors were fooled about the risks of mortgage-backed securities.

The bad news for EMH lovers is that the price is right component is in more trouble than ever. Fischer Black (of Black-Scholes fame) once defined a market as efficient if its prices were ‘within a factor of two of value’ and he opined that by this

(rather loose) definition ‘almost all markets are efficient almost all the time’. Sadly Black died in 1996 but had he lived to see the technology bubble and the bubbles in housing and mortgages he might have amended his standard to a factor of three. Of course, no one can prove that any of these markets were bubbles. But the price of real estate in places such as Phoenix and Las Vegas seemed like bubbles at the time. This does not mean it was possible to make money from this insight. Lunches are still not free. Shorting internet stocks or Las Vegas real estate two years before the peak was a good recipe for bankruptcy, and no one has yet found a way to predict the end of a bubble.

What lessons should we draw from this? On the free lunch component there are two. The first is that many investments have risks that are more correlated than they appear. The second is that high returns based on high leverage may be a mirage. One would think rational investors would have learnt this from the fall of Long Term Capital Management, when both problems were evident, but the lure of seemingly high returns is hard to resist. On the price is right, if we include the earlier bubble in Japanese real estate, we have now had three enormous price distortions in recent memory. They led to misallocations of resources measured in the trillions and, in the latest bubble, a global credit meltdown. If asset prices could be relied upon to always be ‘right’, then these bubbles would not occur. But they have, so what are we to do?

While imperfect, financial markets are still the best way to allocate capital. Even so, knowing that prices can be wrong suggests that governments could usefully adopt automatic stabilising activity, such as linking the down-payment for mortgages to a measure of real estate frothiness or ensuring that bank reserve requirements are set dynamically according to market conditions. After all, the market price is not always right.

Financial Times, 5 August 2009, p. 9.All Rights Reserved.

1 Any share portfolio will perform as well as or better than a special trading rule designed to outperform the market A monkey choosing a portfolio of shares from the Financial Times for a buy and hold strategy is nearly, but not quite, what the EMH advocates suggest as a strat-egy likely to be as rewarding as special inefficiency-hunting approaches. The monkey does not have the financial expertise needed to construct broadly based portfolios which fully diversify away unsystematic risk. A selection of shares in just one or two industrial sectors may expose the investor to excessive risk. So it is wrong to conclude from the EMH evidence that it does not matter what the investor does, and that any portfolio is acceptable. The EMH says that after first eliminating unsystematic risk by holding broadly based portfolios and then adjusting for the residual systematic risk, investors will not achieve abnormal returns.

2 There should be fewer price fluctuations If shares are efficiently priced why is it that they move every day even when there is no announcement concerning a particular company? This is what we would expect in an efficient market. Prices move because new information is coming to the market every hour which may have some influence on the performance of a specific company. For example, the governor of the Bank of England may hint at interest rate rises, the latest industrial output figures may be released and so on.

3 Only a minority of investors are actively trading, most are passive, therefore efficiency cannot be achieved This too is wrong. It only needs a few trades by informed investors using all the publicly available information to position (through their buying and selling actions) a share at its semi-strong-form efficient price.

Implications of the EMH for investors

If the market is efficient there are a number of implications for investors. Even if it is merely effi-cient most of the time, for most participants a sensible working assumption is that pricing is based on fundamental values and the following implications apply.

1 For the vast majority of people public information cannot be used to earn abnormal returns (This refers to returns above the normal level for that systematic risk class.) The implications are that fundamental analysis is a waste of money and that so long as efficiency is maintained the average investor should simply select a suitably diversified portfolio, thereby avoiding costs of analysis and transaction. This message has struck a chord with millions of investors and thousands of billions of pounds have been placed with fund managers who merely replicate a stock market index (index funds) rather than try to pick winners in an actively managed fund. It has been found that the active fund managers generally underperform the market indices – so do the ‘trackers’, but at least they have lower costs.

Another trend has been for small investors to trade shares through execution-only brokers. These brokers do not provide their clients with (nor charge them for) analysis of companies, ‘hot tips’ and suggestions for purchases. They merely carry out the client’s buy or sell orders in the cheapest manner possible.

2 Investors need to press for a greater volume of timely information Semi-strong efficiency depends on the quality and quantity of publicly available information, and so companies should be encouraged by investor pressure, accounting bodies, government rulings and stock market regulation to provide as much as is compatible with the necessity for some secrecy to prevent competitors gaining useful knowledge.

3 The perception of a fair game market could be improved by more constraints and deter-rents placed on insider dealers Strong-form efficiency does not exist and so insiders can gain an unfair advantage.

Implications of the EMH for companiesThe efficient market hypothesis also has a number of implications for companies.

1 Focus on substance, not on short-term appearance Some managers behave as though they believe they can fool shareholders. For example creative accounting is used to show a more



impressive performance than is justified. Most of the time these tricks are transparent to inves-tors, who are able to interpret the real position, and security prices do not rise artificially.

There are some circumstances when the drive for short-term boosts to reported earnings can be positively harmful to shareholders. For example, one firm might tend to overvalue its inventory to boost short-term profitability, another might not write off bad debts. These actions will result in additional, or at least earlier, taxation payments which will be harmful to shareholder wealth. Managers, aware that analysts often pay a great deal of attention to accounting rate of return, may, when facing a choice between a project with a higher NPV but a poor short-term ARR, or one with a lower NPV but higher short-term ARR, choose the latter. This principle of short-termism can be extended into areas such as research and devel-opment or marketing spend. These can be cut to boost profits in the short term but only at a long-term cost to shareholders.

One way to alleviate the short-term/long-term dilemma is for managers to explain why longer-term prospects are better than the current figures suggest. This requires a diligent com-munications effort.

2 The timing of security issues does not have to be fine-tuned Consider a team of managers contemplating a share issue who feel that their shares are currently underpriced because the market is ‘low’. They opt to delay the sale, hoping that the market will rise to a more ‘normal level’. This defies the logic of the EMH – if the market is efficient the shares are already cor-rectly (unbiasedly) priced and the next move in prices is just as likely to be down as up. The past price movements have nothing to say about future movements.

The situation is somewhat different if the managers have private information that they know is not yet priced into the shares. In this case if the directors have good news then they would be wise to wait until after an announcement and subsequent adjustment to the share price before selling the new shares. Bad news announcements are more tricky – to sell the shares to new investors while withholding bad news will benefit existing shareholders, but will result in loss for the new shareholders. There are rules against withholding price sensitive information.

3 Large quantities of new shares can be sold without moving the price A firm wishing to raise equity capital by selling a block of shares may hesitate to price near to the existing share price. Managers may believe that the increase in supply will depress the price of the shares. This is gen-erally not the case. In empirical studies (e.g. Scholes (1972)), if the market is sufficiently large (for example the London or New York Stock Exchange) and investors are satisfied that the new money will generate a return at least as high as the return on existing funds, the price does not fall. This is as we would expect in an efficient market: investors buy the new shares because of the return offered on them for their level of risk.30 The fact that some old shares of the same com-pany already exist and that therefore supply has risen does not come into the equation. The key question is: what will the new shares produce for their holders? If they produce as much as an old share they should be priced the same as an old share. If they are not, then someone will spot that they can gain an abnormal return by purchasing these shares (which will push up the price).

4 Signals from price movements should be taken seriously If, for instance, the directors announce that the company is to take over another firm and its share price falls dramatically on the day of the announcement this is a clear indication that the merger will be wealth destroy-ing for shareholders – as the majority of mergers are (see Chapter 20). Managers cannot ignore this collective condemnation of their actions. An exception might be allowed if shareholders are dumping the shares in ignorance because the managers have special knowledge of the benefits to be derived from the merger – but then shouldn’t the directors explain themselves properly?

Concluding comments

While modern, large and sophisticated stock markets exhibit inefficiencies in some areas, particu-larly at the strong-form level, it is reasonable to conclude that they are substantially efficient and it is rare that a non-insider can outperform the market. One of the more fruitful avenues of future research is likely to concern the influence of psychology on stock market pricing. We have seen

30 Although some studies have shown a decrease in share price when the sale of shares is announced.

how many of the (suggested) semi-strong inefficiencies, from bubbles to underpricing low PER shares, have at their base a degree of apparent ‘non-rationality’. Another line of enquiry is to question the assumption that all investors respond in a similar manner to the same risk and return factors and that these can be easily identified. Can beta be relied upon to represent all relevant risk? If it cannot, what are the main elements investors want additional compensation for? What about information costs, marketability limits, taxes and the degree of covariability with human capital returns for the investor (e.g. earnings from employ-ment)? These are factors disliked by shareholders and so conceivably a share with many of these attributes will have to offer a high return. For some investors who are less sensitive to these ele-ments the share which gives this high return may seem a bargain. A problem for the researcher in this field is that abnormal returns are calculated after allowance for risk. If the model used employs a risk factor which is not fully representative of all the risk and other attributes disliked by investors then efficiency or inefficiency cannot be established. One way of ‘outperforming’ the market might be to select shares the attributes of which you dis-like less than the other investors do, because they are likely to be underpriced for you – given your particular circumstances. Another way is through luck – which is often confused with the third way, that of possessing superior analytical skills. A fourth method is through the discovery of a trading rule which works (but do not tell any-body, because if it becomes widespread knowledge it may stop working, unless it is based on some deep-seated psychological/cognitive error prevalent among investors). A fifth possibility is to be quicker than anyone else in responding to news – George Soros and his teams may fall into this category occasionally. The last, and the most trustworthy method, is to become an insider – the only problem with this method is that you may end up a different kind of insider – in prison. To conclude: the equity markets are generally efficient, but the person with superior analyti-cal ability, knowledge, dedication and creativity can be rewarded with abnormally high returns. However, for people who do not have these four qualities directed effectively at security analysis – the vast majority – it is dangerous to invest or make corporate decisions on the assumption that the share (currency and commodity) markets are inefficient, because most of the time they are efficient. Markets are inefficient in spots. Those spots are first of all difficult to identify, and then, once you think you have identified an area of inefficient pricing it has a tendency to fade away, or additional analysis shows it was not really there in the first place. Playing the game of trying to land yourself in an area of inefficiency is to be played only by the very skilful and knowledgeable. Most corporate managers and fund managers do not qualify.


� In an efficient market security prices rationally reflect available information New information is a rapidly and b rationally incorporated into share prices.

� Types of efficiency:

– operational efficiency;– allocational efficiency;– pricing efficiency.

� The benefits of an efficient market are:

– it encourages share buying;– it gives correct signals to company

managers;– it helps to allocate resources.

� Shares, other financial assets and commodities generally move with a random walk – one day’s price change cannot be predicted by

looking at previous price changes. Security prices respond to news which is random.

� Weak-form efficiency Share prices fully reflect all information contained in past price movements.

Evidence: mostly in support, but there are some important exceptions.

� Semi-strong form efficiency Share prices fully reflect all the relevant, publicly available information.

Evidence: substantially in support but there are some anomalies.

� Strong-form efficiency All relevant information, including that which is privately held, is reflected in the share price.

Evidence: stock markets are strong-form inefficient.

Key points and concepts


� Insider dealing is trading on privileged information. It is profitable and illegal.

� Behavioural finance studies offer insight into anomalous share pricing.

� Implications of the EMH for investors:

– for the vast majority of people public information cannot be used to earn abnormal returns;

– investors need to press for a greater volume of timely information;

– the perception of a fair game market could be improved by more constraints and deterrents placed on insider dealers.

� Implications of the EMH for companies:

– focus on substance, not on short-term appearances;

– the timing of security issues does not have to be fine-tuned;

– large quantities of new shares can be sold without moving the price;

– signals from price movements should be taken seriously.

References and further reading

Abraham, A. and Ikenberry, D. (1994) ‘The individual investor and the weekend effect’, Journal of Financial and Quantitative Analysis, June.

An examination of a particular form of inefficiency.

Al-Rjoub, S.A.M., Varela, O. and Hassan, M.K. (2005) ‘The size reversal in the USA’, Applied Financial Economics, 15, pp. 1189–97.

More evidence on the performance of small capitalisation firms vs. large firms.

Anderson, K. and Brooks, C. (2006) ‘The long-term price–earnings ratio’, Journal of Business Finance and Accounting, 33(7) & (8), pp. 1063–86.

A PER effect with a difference – shows a high return to shares with a low share price relative to the previous eight years of earnings.

Andrikopoulos, P., Daynes, A., Latimer, D. and Pagas, P, (2008) ‘Size effect, methodological issues and “risk-to-default”: evidence from the UK stock market’, European Journal of Finance, 14(4) pp. 299–314.

While a small firm effect is shown it is regarded as ‘unreliable’.

Arnold, G. (2010) The Great Investors. Harlow: FT Prentice Hall.

Explains eight investment philosophies by nine very successful investors, including Warren Buffett and George Soros.

Arnold, G.C. (2009) The Financial Times Guide to Value Investing, 2nd Edition. London: Financial Times Prentice Hall.

Brings together the insights from successful investors, finance theory and strategic analysis.

Arnold, G.C. and Baker, R.D. (2007) ‘Return reversal in UK shares’, Salford Business School Working Paper 107/07.

Shows evidence supporting the view that investors in shares with the worst recent five-year returns outperform in the subsequent five years (on average).

Arnold, G. and Shi, J. (2005) ‘Profitability of momentum strategies in UK bull and bear market conditions’, University of Salford Working Papers.

Momentum effects are present in bull and bear markets.

Arnold, G.C. and Xiao, Y. (2007) ‘Financial statement analysis and the return reversal effect’. Salford Business School Working Paper 108/07.

Shows evidence indicating that portfolios of ‘loser’ shares (those that give the lowest returns over five years) which also have strong financial variables (e.g. positive cash flow or improving financial gearing) outperform those with poor financial fundamentals.

Atkins, A.B. and Dyl, E.A. (1993) ‘Reports of the death of the efficient markets hypothesis are greatly exaggerated’, Applied Financial Economics, 3, pp. 95–100.

A consideration of some key issues.

Baba, N. and Kozaki, M. (1992) ‘An intelligent forecasting system of stock prices using neural networks’, Proceedings of International Joint Conference on Neural Networks, Baltimore, MD, vol. 1, pp. 371–7.

Evidence on a possible inefficiency.

Ball, R. (1995) ‘The theory of stock market efficiency: Accomplishments and limitations’, Journal of Applied Corporate Finance, Winter and Spring, pp. 4–17.

Interesting discussion.

Ball, R. (2001) ‘The theory of stock market efficiency: accomplishments and limitations’, in Chew, D.H. (ed.) The New Corporate Finance, 3rd edn. New York: McGraw-Hill Irwin.

An interesting overview of how far we have come in understanding the efficiency of stock markets.


Ball, R. and Brown, P. (1968) ‘An empirical evaluation of accounting income numbers’, Journal of Accounting Research, Autumn, pp. 159–78.

The stock market turns to other sources of information to value shares so that when the annual report is published it has little effect on prices.

Ball, R. and Kothari, S.P. (1989) ‘Nonstationary expected returns: Implications for tests of market efficiency and serial correlation in returns’, Journal of Financial Economics, 25, pp. 51–94.

Negative serial correlation in relative returns is due largely to changing relative risks and thus changing expected returns.

Ball, R., Kothari, S.P. and Shanken, J. (1995) ‘Problems in measuring portfolio performance: An application to contrarian investment strategies’, Journal of Financial Economics, May, vol. 38, pp. 79–107.

Performance measurement problems cast doubt on the overreaction study results.

Banz, R. (1981) ‘The relationship between return and market value of common stock’, Journal of Financial Economics, 9, pp. 3–18.

Important early paper on the small firm effect.

Banz, R.W. and Breen, W.J. (1986) ‘Sample-dependent results using accounting and market data: Some evidence’, Journal of Finance, 41, pp. 779–93.

A technical article concerned with the problem of bias when using accounting information (earnings). The bias in the data can cause the low PER effect.

Barber, B.M. and Odean, T. (1999) ‘The courage of misguided convictions’, Financial Analysts Journal, 55, November–December, pp. 41–55.

Investors who trade frequently perform worse than those who trade little. Support for over-confidence hypothesis.

Barber, B. and Odean, T. (2000) ‘Trading is hazardous to your wealth: the common stock investment performance and individual investors’, Journal of Finance, 55(2), April, pp. 773–806.

Investors who trade a lot perform worst.

Barberis, N., Shleifer, A. and Vishny, R.W. (1998) ‘A model of investor sentiment’, Journal of Financial Economics, 49, pp. 307–43.

A theoretical model based on behaviotral finance ideas in which investors believe at times that the market is trending and at other times it is mean-reverting (draws on representativeness and conservatism).

Barclays Capital (annual) Equity Gilt Study. London: Barclays Capital.

Important source of data on share and other security returns and risks.

Basu, S. (1975) ‘The information content of price-earnings ratios’, Financial Management, 4, Summer, pp. 53–64.

Evidence of a market inefficiency for low PER shares.

However transaction costs, search costs and taxation prevent abnormal returns.

Basu, S. (1977) ‘Investment performance of common stocks in relation to their price/earnings ratios: A test of the efficient market hypothesis’, Journal of Finance, 32(3), June, pp. 663–82.

Low PER portfolios earn higher absolute and risk-adjusted rates of return than high PER shares. Information was not fully reflected in share prices.

Basu, S. (1983) ‘The relationship between earnings’ yield, market value and return for NYSE stocks – Further evidence’, Journal of Financial Economics, June, pp. 129–56.

The PER effect subsumes the size effect when both variables are considered jointly.

Benartzi, S. and Thaler, R. (1995) ‘Myopic loss aversion and the equity premium puzzle’, Quarterly Journal of Economics, 110(1), pp. 73–92.

Narrow framing leads to unreasonable risk aversity and too little investment in equities.

Bernard, V. (1993) ‘Stock price reaction to earnings announcements’, in Thaler, R. (ed.) Advances in Behavioural Finance. New York: Russell Sage Foundation.

Sluggish response.

Bernard, V.L. and Thomas, J.K. (1989) ‘Post-earnings-announcement drift: Delayed price response or risk premium?’, Journal of Accounting Research, 27 (Supplement 1989), pp. 1–36.

A study showing slow reaction to unexpected earnings figures indicating inefficiency.

Bernstein, P.L. (1996) Against the Gods: The Remarkable Story of Risk. Chichester: John Wiley & Sons, Inc.

Chronicles the rise of the tools of modern risk management.

Black, F. (1986) ‘Noise’, Journal of Finance, 41(3), July, pp. 529–34.

A large number of small events is often a causal factor much more powerful than a small number of large events.

Bris, A. (2005) ‘Do insider trading laws work?’ European Financial Management, 11(3) pp. 267–312.

A study of the effect of the enforcement of insider trading laws across the world.

Brock, W., Lakonishok, J. and LeBaron, B. (1992) ‘Simple technical trading rules and the stochastic properties of stock returns’, Journal of Finance, 47, December, pp. 1731–64.

Some interesting evidence suggesting weak-form inefficiency.

Brown, S.J., Goetzmann, W.N. and Kumar, A. (1998) ‘The Dow theory: William Peter Hamilton’s track record reconsidered’, Journal of Finance, 53(4), pp. 1311–33.

Some positive results for the Dow theory.

Brunnermeier, M.K. and Nagel, S. (2004) ‘Hedge funds and the technology bubble’, Journal of Finance LIX (5), October, pp. 2013–40.


Rational investors are not acting as arbitrageurs to return share prices to an efficient level – they reinforce inefficient pricing helping to destabilise.

Brusa, J., Liu, P. and Schulman, C. (2003) ‘The weekend and ‘reverse” weekend effects: An analysis by month of the year, week of month, and industry’, Journal of Business Finance and Accounting, 30(5) and (6), June/July, pp. 863–90.

Findings: weekend and reverse weekend effects are shown for US share indices.

Buffett, W.E. (1984) ‘The superinvestors of Graham-and-Doddsville’, an edited transcript of a talk given at Columbia University in 1984.

Reproduced in Hermes, the magazine of Columbia Business School, Fall 1984 and in the 1997 and 2003 reprints of Graham (1973).

Buffett, W.E. (2000) Letter to shareholders included with the 2000 Annual Report of Berkshire Hathaway Inc: www.berkshirehathaway.com.

High-quality thinking and writing from the world’s most successful investor.

Capaul, C., Rowley, I. and Sharpe, W.F. (1993) ‘International value and growth stock returns’, Financial Analysts Journal, 49, January–February, pp. 27–36.

Evidence on returns from a book-to-market ratio strategy for France, Germany, Switzerland, the UK and Japan.

Chan, A. and Chen, A.P.L. (1996) ‘An empirical re-examination of the cross-section of expected returns: UK evidence’, Journal of Business Finance and Accounting, 23, pp. 1435–52.

High divided yields associated with high share returns.

Chan, L.K.C. and Lakonishok, J. (2004) ‘Value and growth investing: review and update’, Financial Analysts Journal, January/February, pp. 71–86.

An overview of the value versus growth empirical evidence plus some recent evidence.

Chan, L.K.C., Hamao, Y. and Lakonishok, J. (1991) ‘Fundamentals and stock returns in Japan’, Journal of Finance, 46, pp. 1739–64.

The book-to-market ratio and cash flow yield have influences on the returns. There is a weak size effect and a doubtful PER effect.

Chan, L.K.C. Jegadeesh, N. and Lakonishok, J. (1996) ‘Momentum strategies’, Journal of Finance, 51, December, pp. 1681–713.

Underreaction to both past share returns and earnings surprises.

Chew, D.H. (ed.) (1993) The New Corporate Finance. New York: McGraw-Hill.

Contains a number of easy-to-read articles on efficiency.

Chopra, N., Lakonishok, J. and Ritter, J.R. (1992) ‘Measuring abnormal performance: Do stocks overact?’, Journal of Financial Economics, 31, pp. 235–68.

Overreaction effect observed.

Chordia, T., Goyal, A., Sadka, G., Sadka, R. and Shivakumar, L. (2009) ‘Liquidity and the post-earnings-announcement drift’, Financial Analysts Journal, 65(4) pp. 18–32.

Post-earnings-announcement drift occurs mainly in highly liquid shares.

Chui, A.C.W., Titman, S. and Wei, K.C.J. (2010) ‘Individualism and momentum around the world’, The Journal of Finance, LXV(1), Feb. pp. 361–92.

Individualism and self-attribution bias are related to overconfidence, which in turn is related to momentum profits.

Clare, A. and Thomas, S. (1995) ‘The overreaction hypothesis and the UK stock market’, Journal of Business Finance and Accounting, 22(7), October, pp. 961–73.

Overreaction occurs, but it is a manifestation of the small firm effect.

Cuthbertson, K. (2004) Quantitative Financial Economics, 2nd edn. Chichester: Wiley.

Contains a more rigorous mathematical treatment of the issues discussed in this chapter.

Daniel, K. and Titman, S. (1997) ‘Evidence on the characteristics of cross sectional variation in stock returns’, Journal of Finance, 52(1), March, p. 1–33.

The high returns to high book-to-market ratio shares and small market capitalisation shares is not a result of compensation for risk (opposing Fama and French’s view and supporting the behavioural finance view).

Daniel, K., Hirshleifer, D. and Subrahmanyam, A. (1998) ‘Investor psychology and security market under- and overreactions’, Journal of Finance, 53(6), pp. 1839–85.

Behavioural explanation of inefficiencies. Under- and overreaction are due to the psychological biases of investor overconfidence and biased self-attributes.

Davis, J.L., Fama, E.F. and French, K.R. (2009) ‘Characteristics, covariances, and average returns: 1929 to 1997’, The Journal of Finance, LV(1), pp. 389–406.

‘The value premium in US stock returns is robust’. Studying book-to-market ratio.

Dawson, E.R. and Steeley, J.M. (2003) ‘On the existence of visual technical patterns in the UK stock market’, Journal of Business Finance and Accounting, 30(1) and (2), January–March, pp. 263–97.

Failure to find profitable trading rules based on technical patterns.

De Bondt, W.F.M. and Thaler, R.H. (1985) ‘Does the stock market overreact?’, Journal of Finance, 40(3), July, pp. 793–805.

An important paper claiming weak-form inefficiency.

De Bondt, W.F.M. and Thaler, R.H. (1987) ‘Further evidence on investor overreaction and stock market seasonality’, Journal of Finance, 42(3), pp. 557–81.

Overreaction effect observed.

Dellavigna, S. and Pollet, J.M. (2009), ‘Investor inattention and Friday earnings announcements’, The Journal of Finance, LXIV(2), pp. 709–49.


Post-earnings-announcement drift is strong for Friday announcements.

De Long, J.B., Shleifer, A., Summers, L.H. and Waldmann, R.J. (1989) ‘The size and incidence of the losses from noise trading’, Journal of Finance, 44(3), July, pp. 681–96.

Noise trading by naive investors can lead to costs for society.

De Long, J.B., Shleifer, A., Summers, L.H. and Waldmann, R.J. (1990) ‘Noise trader risk in financial markets’, Journal of Political Economy, 98, pp. 703–38.

Discussing the risk that irrational ill-informed investors may push prices further away from fundamental value thus throwing the arbitrageurs’ trading strategies.

Dimson, E. (ed.) (1988) Stock Market Anomalies. Cambridge: Cambridge University Press.

A collection of 19 important articles questioning stock market efficiency.

Dimson, E. and Marsh, P.R. (1986) ‘Event study methodologies and the size effect: The case of UK press recommendations’, Journal of Financial Economics, 17, pp. 113–42.

UK small firm shares outperformed those of larger firms.

Dimson, E. and Marsh P.R. (1999) ‘Murphy’s law and market anomalies’, Journal of Portfolio Management, 25(2), pp. 53–69.

Small companies outperformed large companies until the 1980s, then they underperformed.

Dimson, E., Marsh, P.R. and Staunton, M. (2001) The Millennium Book II: 101 Years of Investment Returns. London: ABN AMRO and London Business School.

Shows returns on shares and other securities over the twentieth century. The section on small firms shows a reversal of the small-firm effect.

Dimson, E., Marsh, P.R. and Staunton, M. (2002) The Triumph of the Optimists: 101 Years of Global Investment Returns. Princeton, NJ: Princeton University Press.

An important study on market returns with a chapter on the small firm effect.

Dimson, E., Marsh, P. and Staunton, M. (2008) ABN AMRO Global Investment Returns Yearbook 2009. ABN AMRO, Royal Bank of Scotland and London Business School.

Evidence on price momentum stretching back over 100 years.

Dimson, E., Marsh, P. and Staunton, M. (2009) Credit Suisse Global Investment Returns Yearbook 2009. London: Credit Suisse. Available at https://emagazine.credit-suisse.com.

Evidence on the size effect and the value effect.

Dissanaike, G. (1997) ‘Do stock market investors overreact?’, Journal of Business Finance and Accounting, 24(1), January, pp. 27–49.

Buying poor-performing shares gives abnormal returns as they are underpriced due to investor overreaction (UK study).

Dreman, D. (1998) Contrarian Investment Strategies: The next generation. New York: Simon & Schuster.

A sceptic’s view on efficiency.

Dreman, D. and Berry, M. (1995) ‘Overreaction, underreaction, and the low P/E effect’, Financial Analysts Journal, 51, July/August, pp. 21–30.

Overreaction and underreaction shown.

Economist, The (1992) ‘Beating the market: Yes – it can be done’, The Economist, 5 December.

Good survey of the evidence on the EMH and CAPM. Easy to read.

Elton, E.J., Gruber, M.J. and Rentzler, J. (1983) ‘A simple examination of the empirical relationship between dividend yields and deviations from the CAPM’, Journal of Banking and Finance, 7, pp. 135–46.

Complex statistical analysis leads to the conclusion: ‘We have found a persistent relationship between dividend yield and excess returns.’

Elton, E.J., Gruber, M.J., Brown, S.J. and Goetzmann, W.N. (2003) Modern Portfolio Theory and Investment Analysis, 6th edn. New York: Wiley.

A more technical treatment than that in this chapter.

Fama, E.F. (1965) ‘The behaviour of stock market prices’, Journal of Business, January, pp. 34–106.

Leading early article that first defined market efficiency.

Fama, E.F. (1970) ‘Efficient capital markets: A review of theory and empirical work’, Journal of Finance, May, pp. 383–417.

A review of the early literature and a categorisation of efficiency.

Fama, E.F. (1991) ‘Efficient capital markets II’, Journal of Finance, 46(5), December, pp. 1575–617.

A review of the market efficiency literature with a strong bias in favour of the view that the market is efficient.

Fama, E.F. (1998) ‘Market efficiency, long-term returns, and behavioural finance’, Journal of Financial Economics, 49, September, pp. 283–306.

Anomalies are explained and efficiency is championed.

Fama, E.F. and French, K.R. (1988) ‘Permanent and temporary components of stock prices’, Journal of Political Economy, 96, pp. 246–73.

Useful.

Fama, E.F. and French, K.R. (1992) ‘The cross-section of expected stock returns’, Journal of Finance, 47, pp. 427–65.

An excellent study casting doubt on beta and showing size of company and book-to-market ratio affecting returns on shares.

Fama, E.F. and French, K.R. (1995) ‘Size and book-to-market factors in earnings and returns’, Journal of Finance, 50(1), pp. 131–55.

Higher returns to smaller companies and those with high book-to-market ratios. These are described as risk factors and so, it is argued, efficiency is maintained.

Fama, E.F. and French, K.R. (1996) ‘Multifactor explanations of asset pricing anomalies’, Journal of Finance, 50(1), March, pp. 55–84.

Efficiency is retained – size and book-to-market are risk factors.


Fama, E.F. and French, K.R. (1998) ‘Value versus growth: The international evidence’, Journal of Finance, 53(6), December, pp. 1975–99.

An average return on global portfolios of high and low book-to-market shares is 7.68 per cent per year. Explanation: additional distress risk.

Fama, E.F. and French, K.R. (2006) ‘The value premium and the CAPM’, Journal of Finance, LXI (5) October, pp. 2163–85.

Value shares (defined by low price-to-earning ratio or book-to-market ratio) outperform in the USA and in other countries – and they have lower betas.

Fama, E.F. and French, K.R. (2008) ‘Average returns, B/M, and share issues’, The Journal of Finance, LXIII(6), December, pp. 2971–95.

The way in which the book-to-market ratio changed over the previous few years can impact on share returns to reinforce the tendency of value to outperform growth.

Fama, E.F., Fisher, L., Jensen, M.C. and Roll, R. (1969) ‘The adjustment of stock prices to new information’, International Economic Review, 10(1), February, pp. 1–21.

Investigates the adjustment of share prices to the information which is implicit in share splits. Evidence of semi-strong EMH.

Fifield, S.G.M., Power, D.M. and Sinclair, C.D. (2005) ‘An analysis of trading strategies in eleven European stock markets’, European Journal of Finance, 11(6) pp. 531–48.

Investigates weak-form efficiency and finds inefficiency in less developed markets.

Figelman, I. (2007) ‘Interaction of stock return momentum with earnings measures’, Financial Analysts Journal, 63(3), pp. 71–8.

Momentum evidence.

Firth, M.A. (1977a) ‘An empirical investigation of the impact of the announcement of capitalisation issues on share prices’, Journal of Business Finance and Accounting, Spring, p. 47.

Scrip issues in themselves have no impact on share prices. Evidence that the stock market is efficient.

Firth, M.A. (1977b) The Valuation of Shares and the Efficient Markets Theory. Basingstoke: Macmillan.

An early discussion of stock market efficiency.

Foster, G. (1979) ‘Briloff and the capital markets’, Journal of Accounting Research, 17, pp. 262–74.

An elegantly simple investigation of the effect of one man’s pronouncement on stock market prices.

Foster, G., Olsen, C. and Shevlin, T. (1984) ‘Earnings releases, anomalies, and the behaviour of security returns’, Accounting Review, 59(4), October, pp. 574–603.

A delayed response of share prices to earnings surprise news.

Fox, J. (2009) The Myth of the Rational Market. London: HarperBusiness.

‘Chronicles the rise and fall of the efficient market theory’ in a very easy-to-read fashion, bringing to life the key players and their contributions to the debate.

Frazzini, A. (2006) ‘The disposition effect and underreaction to news’, Journal of Finance, LXI (4), August, pp. 2017–46.

Provides a behavioural finance explanation for post-announcement drift.

Froot, K.A. and Dabora, E. (1999) ‘How are stock prices affected by the location of trade?’, Journal of Financial Economics, 53, pp. 189–216.

Evidence of noise trader risk.

Fuller, R.J., Huberts, L.C. and Levinson, M.J. (1993) ‘Returns to E/P strategies, higgledy-piggledy growth, analysts’ forecast errors, and omitted risk factors’, Journal of Portfolio Management, Winter, pp. 13–24.

Regression to the mean of earnings growth shown for US companies classified by PER.

George, T.J. and Hwang, C. (2007) ‘Long-term return reversals: overreaction or taxes?’ The Journal of Finance, LXII(6), pp. 2865–96.

A return reversal effect in US shares is found and an explanation provided, based around a capital gains argument.

Graham, B. (1973, revised 2003) The Intelligent Investor, revised edition, updated by Jason Zweig. New York: Harper Business Essentials.

The classic value investing book.

Graham, B. and Dodd, D. (1934) Security Analysis. New York: McGraw-Hill.

The foundation stone for value investors.

Gregory, A., Harris, R.D.F. and Michou, M. (2001) ‘An analysis of contrarian investment strategies in the UK’, Journal of Business Finance and Accounting, 28(9) and (10), November–December, pp. 1193–228.

Value shares outperform.

Gregory, A., Harris, R.D.F. and Michou, M. (2003) ‘Contrarian investment and macroeconomic risk’, Journal of Business Finance and Accounting, 30(1) and (2), January–March, pp. 213–55.

Grinblatt, M. and Han, B. (2005) ‘Prospect theory, mental accounting and momentum’, Journal of Financial Economics, 78, pp. 311–39.

Uses behavioural finance models to explain the momentum phenomenon in shares.

Hamberg, M. and Novak, J. (2010) ‘Accounting conservatism and transitory earnings in value and growth strategies’, Journal of Business Finance & Accounting, 37(5), (6), pp. 518–37.

Swedish value shares (earnings-to-price ratio or book-to-market ratio) outperform growth shares.

Harris, A. (1996) ‘Wanted: Insiders’, Management Today, July, pp. 40–1.

A short and thought-provoking article in defence of insider dealing.

Hawawini, G.A. and Michel, P.A. (eds) (1984) European Equity Markets, Risk, Return and Efficiency. Garland Publishing.

A collection of articles and empirical work on the behaviour of European equity markets.


Hawawini, G. and Klein, D.B. (1994) ‘On the predictability of common stock returns: Worldwide evidence’, in Jarrow, R.A., Maksinovic, V. and Ziembas, W.T. (eds) Finance. Amsterdam: North-Holland.

More evidence on inefficiency.

Hirshleifer, D., Lim, S.S. and Teoh, S.H. (2009) ‘Driven to distraction: extraneous events and underreaction to earnings news’, The Journal of Finance, LXIV(5), pp. 2289–325.

Post-earnings-announcement drift is stronger when there are a number of earnings announcements made by other firms on the same day.

Hon, M.T. and Tonks, I. (2003) ‘Momentum in the UK stock market’, Journal of Multinational Financial Management, 13, pp. 43–70.

Momentum of share returns is not present in all periods of stock market history.

Hong, H. and Stein, J.C. (1999) ‘A unified theory of underreaction, momentum trading and overreaction in asset markets’, Journal of Finance, 54(6), pp. 2143–84.

Behavioural explanation of inefficiencies. A model in which information diffuses gradually across the investing population is used to provide an explanation for underreaction and then overreaction.

Ikenberry, D., Lakonishok, J. and Vermaelen, T. (1995) ‘Market under reaction to open market share repurchases’, Journal of Financial Economics, October–November, pp. 181–208.

Share price drift after share repurchase announcements.

Ikenberry, D., Rankine, G. and Stice, E. (1996) ‘What do stock splits really signal?’, Journal of Financial and Quantitative Analysis, 31, pp. 357–75.

Share price drift evidence.

Jaffe, J., Keim, D.B. and Westerfield, R. (1989) ‘Earnings yields, market values and stock returns’, Journal of Finance, 44, pp. 135–48. US data, 1951–86.

Finds significant PER and size effects (January is a special month).

Jegadeesh, N. and Titman, S. (1993) ‘Returns to buying winners and selling losers: Implications for stock market efficiency’, Journal of Finance, 48, March, pp. 65–91.

Holding shares which have performed well in the past generates significant abnormal returns over 3–12-month holding periods.

Jensen, M.C. (1968) ‘The performance of mutual funds in the period 1945–64’, Journal of Finance, 23, May, pp. 389–416.

Mutual funds were poor at predicting share prices and underperformed the market.

Kahnemann, D. and Tversky, A. (2000) Choices, Values and Frames. Cambridge: Cambridge University Press.

An important book on behavioural finance.

Kahneman, D., Slovic, P. and Tversky, A. (1982) Judgment under Uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press.

A collection of classic articles on decision making which have strongly influenced the behavioural finance field.

Kama, I. (2009) ‘On the market reaction to revenue and earnings surprises’, Journal of Business Finance & Accounting, 36(1), (2), pp. 31–50.

More evidence of post-earnings announcement drift.

Kamijo, K.-I. and Tanigawa, T. (1990) ‘Stock price recognition – approach’, Proceedings of International Joint Conference on Neural Networks, San Diego, CA, vol. 1, pp. 215–21.

Evidence on a potential inefficiency.

Kaplan, R. and Roll, R. (1972) ‘Investor evaluation of accounting information: Some empirical evidence’, Journal of Business, 45, pp. 225–57.

Earnings manipulation through accounting changes has little effect on share prices.

Kay, J. (2009) The Long and Short of It. London: The Erasmus Press.

An impressive, easy-to-read book, which while explaining the basics of finance/investment also attacks the current set-up that often does not serve the interests of investors. ‘You cannot be an intelligent investor if you believe that markets are always efficient or deny that they are mostly efficient. It is a big mistake to believe that the efficient market hypothesis is true, and a bigger mistake to believe that it is false.’

Keim, D.B. (1983) ‘Size-related anomalies and stock return seasonality: Further empirical evidence’, Journal of Financial Economics, 12, pp. 13–32.

Small-firm effect.

Keim, D.B. (1988) ‘Stock market regularities: A synthesis of the evidence and explanations’, in Dimson, E. (ed.) Stock Market Anomalies, Cambridge: Cambridge University Press, and in Lofthouse, S. (ed.) (1994) Readings in Investment, Chichester: Wiley.

A non-technical, easy to understand consideration of some evidence of market inefficiencies.

Keim, D.B. and Ziemba, W.T. (eds) (2000) Security Market Imperfections in World Wide Equity Markets. Cambridge: Cambridge University Press.

A collection of empirical articles on the evidence on efficiency.

Kendall, M. (1953) ‘The analysis of economic time-series prices’, Journal of the Royal Statistical Society, 96, pp. 11–25.

Classic founding article on random walks.

Keynes, J.M. (1936) The General Theory of Employment, Interest and Money. London: Harcourt, Brace and World.

A classic economic text with some lessons for finance.

Kindleberger, C.P. and Aliber, R.Z. (2011) Manias, Panics and Crashes: A History of Financial Crises, 6th edn. New York: Macmillan.

Study of the history of odd market behaviour.


Kothari, S.P., Shanken, J. and Sloan, R.G. (1995) ‘Another look at the cross-section of expected stock returns’, Journal of Finance, 50(1) March, pp. 185–224.

Apparent excess returns disappear if risk is allowed for.

Lakonishok, J., Shleifer, A. and Vishny, R. (1994) ‘Contrarian investment extrapolation and risk’, Journal of Finance, 49, pp. 1541–78.

Value share outperformance.

Lamont, O.A. and Thaler, R.H. (2003) ‘Can the market add and subtract? Mispricing in tech price equity carve-outs’, Journal of Political Economy, 111 (2 April), pp. 227–68.

Examples of odd pricing by the market: e.g. 3Com held a proportion of Palm’s shares, yet 3Com was valued by the market at less than the Palm shareholding – a rational market?

La Porta, R. (1996) ‘Expectations and the cross-section of stock returns’, Journal of Finance, 51(5), December,pp. 1715–42.

‘I show that investment strategies that seek to exploit errors in analysts’ forecasts earn superior returns.’

La Porta, R., Lakonishok, J., Shleifer, A. and Vishny, R. (1997) ‘Good news for value stocks: Further evidence on market efficiency’, Journal of Finance, 52(2), pp. 859–74.

Earnings surprises are more positive for value shares: ‘The evidence is inconsistent with risk-based explanation for the return differential.’

Lee, D.R. and Verbrugge, J.A. (1996) ‘The efficient market theory thrives on criticism’, Journal of Applied Corporate Finance, 9(1), pp. 3–11.

An overview of efficiency evidence.

Lerman, A., Livnat, J. and Mendenhall, R.R. (2007), ‘Double surprise into higher future returns’, Financial Analysts Journal, 63(4), pp. 63–71.

Post-earnings-announcement drift is greater when analysts’ forecasts are used rather than historical earnings data to estimate the extent of the surprise.

Levis, M. (1989) ‘Stock market anomalies: A reassessment based on UK evidence’, Journal of Banking and Finance, 13, pp. 675–96.

Shows that strategies based on dividend yield, PE ratios and share prices appear to be as profitable as (if not more so than) a strategy of concentrating on firm size.

Lewellen, J. (2004) ‘Predicting returns with financial ratios’ Journal of Financial Economics, 74, pp. 209–35.

Evidence that higher returns are attainable by buying high dividend yield shares, high earnings–price ratio shares or high book to market value shares.

Li, X., Brooks, C. and Miffre, J. (2009) ‘The value premium and time-varying volatility’, Journal of Business Finance & Accounting, 36(9), (10), pp. 1252–72.

Value shares outperforming growth shares. Examines earnings yield, book-to-market and cash flow to price as determining factors.

Little, I.M.D. (1962) ‘Higgledy piggledy growth’, Institute of Statistics Bulletin, 24(4), pp. 387–412.

Profit trends for companies are unreliable.

Liu, W., Strong, N. and Xu, X. (1999) ‘The profitability of momentum investing’, Journal of Business Finance and Accounting, 26(9) and (10), November–December, pp. 1043–91.

Following a price momentum strategy was profitable over the period 1977 to 1998.

Liu, W., Strong, N. and Xu, X. (2003) ‘Post-earnings-announcement drift in the UK’, European Financial Management, 9(1), pp. 89–116.

Post-earnings-announcement drift evident in the UK.

Liu, Y., Szewczyk, S.H. and Zantout, Z. (2008) ‘Underreaction to dividend reductions and omissions?’ The Journal of Finance, LXIII(2), pp. 987–1020.

‘This study reports significantly negative post-earnings announcement long-term abnormal returns’ following dividend reductions or omissions.

Litzenberger, R.H. and Ramaswamy, K. (1979) ‘The effect of personal taxes and dividends on capital asset prices: Theory and empirical evidence’, Journal of Financial Economics, 7, pp. 163–95.

Technical paper with the conclusion: ‘There is a strong positive relationship between dividend yield and expected return for NYSE stocks.’

Lo, A.W. and Mackinley, A.C. (2001) A Non-random Walk Down Wall Street. Princeton, NJ: Princeton University Press.

A challenge to the random walk hypothesis – they claim some predictability.

Lo, A.W. and Hasanhodzic, J. (2010) The Evolution of Technical Analysis: Financial Prediction from Babylonian Tablets to Bloomberg Terminals. New York: John Wiley and Sons.

Explores the fascinating history of technical analysis, tracing where technical analysts failed, how they succeeded, and what it all means for today’s traders and investors.

Lofthouse, S. (2001) Investment Management, 2nd edn. Chichester: John Wiley & Sons.

Great for those interested in financial market investment. Transparently clear explanations of complex material.

Lofthouse, S. (ed.) (1994) Readings in Investment. Chichester: John Wiley & Sons.

A superb book for those keen on understanding stock market behaviour. A collection of key papers introduced and set in context by Stephen Lofthouse.

Lowe, J. (1997) Warren Buffett Speaks. New York: John Wiley & Sons.

Terrific quotations from Buffett.

Lowe, J. (1999) The Rediscovered Benjamin Graham. New York: John Wiley & Sons.

Some observations from the most respected practitioner/intellectual, compiled by Janet Lowe.

Lynch, P. (1990) One Up on Wall Street (with John Rothchild). New York: Penguin Books. (Originally published by Simon & Schuster, 1989.)

Fascinating insight into the world of stock picking. Presents sound investment principles.


Lynch, P. (1994) Beating the Street (with John Rothchild). New York: Simon & Schuster.

Revised version of 1993 hardback publication. Fascinating insight into the world of stock picking. Presents sound investment principles.

Malkiel, B.G. (1999) A Random Walk Down Wall Street. New York: W.W. Norton & Co.

A superb introduction to the theory and reality of stock market behaviour. A witty prose description of the arguments for and against EMH.

Martikainen, T. and Puttonen, V. (1996) ‘Finnish days-of-the-week effects’, Journal of Business Finance and Accounting, 23(7), September, pp. 1019–32.

There is evidence of a day-of-the-week effect in the cash and derivative markets.

Michaely, R., Thaler, R. and Womack, K. (1995) ‘Price reaction to dividend initiations and omissions: Overreaction or drift?’, Journal of Finance, 50, pp. 573–608.

Share price drift evidence.

Michou, M. (2009) ‘Is the value spread a good predictor of stock returns? UK evidence’, Journal of Business Finance & Accounting, 36(7), (8), pp. 925–50.

More evidence that the book-to-market ratio can explain high returns to value shares.

Miles, D. and Timmerman, A. (1996) ‘Variations in expected stock returns: evidence on the mispricing of equities from a cross-section of UK companies’, Economica, 63, pp. 369–82.

Some interesting evidence and discussion.

Montier, J. (2002) Behavioural Finance: Insights into Irrational Minds and Markets. London: John Wiley & Sons.

A very good overview of the usefulness of developments in the decision-making under uncertainty literature in the real world of fund management. Written by a practising equity strategist.

Montier, J. (2009) Value Investing: Tools and Techniques for Intelligent Investment. Chichester: John Wiley & Sons Ltd.

From the pen of a shrewd observer of markets. A practitioner’s insights into the impact of human behavioural traits on market prices. Very definitely not a fan of EMH.

Montier, J. (2010) The Little Book of Behavioral Investing. Chichester: John Wiley & Sons, Inc.

A short book on impact of human behaviour on market prices, written by a knowledgeable and experienced practitioner.

Morgan, G. and Thomas, S. (1998) ‘Taxes, dividend yields and returns in the UK equity market’, Journal of Banking and Finance, 22, pp. 405–23.

High dividend yield is correlated with high returns.

Mussweiler, T. and Schneller, K. (2003) ‘“What goes up must come down” – how charts influence decisions to buy and sell stocks’, The Journal of Behavioral Finance, 4(3), pp. 121–30.

Weak for efficiency challenged.

Neff, J. (1999) John Neff on Investing (with S.L. Mintz). New York: John Wiley & Sons.

Decades of investing experience create a very interesting book to guide aspiring investors. Insight into investor/market behaviour.

Park, C-H. and Irwin, S.H. (2007) ‘What do we know about the profitability of technical analysis?’ Journal of Economic Surveys, 21 (4), pp. 786–826.

Examines a great array of literature testing weak form efficiency – the more recent evidence generally supports the technical analyst’s view that the share, currency and commodity markets examined are inefficient in many ways.

Peters, E.E. (1991) Chaos and Order in the Capital Markets. New York: John Wiley & Sons.

A comprehensible account of chaos theory applied to market pricing. The evidence is not powerful enough to demolish the EMH.

Phalippou, L. (2008) ‘Where is the value premium?’ Financial Analysts Journal, 64(2), pp. 41–8.

The book-to-market ratio effect is concentrated in just 7 per cent of shares.

Piotroski, J. D. (2000) ‘Value investing: the use of historical financial statement information to separate winners from losers’, Journal of Accounting Research, 38, Supplement, pp. 1–51.

Piotroski uses nine accounting variables (e.g. positive cash flow) to classify high book-to-market ratio shares into different categories of financial strength. He finds evidence that the market does not properly incorporate these financial strength factors because ‘strong’ company shares significantly outperform ‘weak’ company shares.

Pontiff, J. and Schall, L.D. (1998) ‘Book-to-market ratios as predictors of market returns’, Journal of Financial Economics, 49, pp. 141–60.

Book-to-market ratios predict market returns and small-firm excess returns.

Poterba, J.M. and Summers, L.H. (1988) ‘Mean reversion in stock prices: Evidence and implications’, Journal of Financial Economics, 22, pp. 27–59.

The idea that share returns eventually revert to the average.

Puetz, A. and Ruenzi, S. (2011) ‘Overconfidence among professional investors: evidence from mutual fund managers’, Journal of Business Finance & Accounting, Jun/Jul, 38(5/6), pp. 684–712.

‘Consistent with theories of overconfidence, we find that fund managers trade more after good past performance.’

Reinganum, M.R. (1981) ‘Misspecification of capital asset pricing: Empirical anomalies based on earnings’ yields and market values’, Journal of Financial Economics, 9, pp. 19–46.

The PER effect disappears when size is simultaneously considered.

Reinganum, M.R. (1988) ‘The anatomy of a stock market winner’, Financial Analysts Journal, March–April, pp. 272–84.

More on inefficiencies due to low net assets.


Rendleman, R.J., Jones, C.P. and Latané, H.E. (1982) ‘Empirical anomalies based on unexpected earnings and the importance of risk adjustments’, Journal of Financial Economics, November, pp. 269–87.

Abnormal returns could have been earned by exploiting the slow response to unexpected earnings figures.

Ridley, M. (1993) ‘Survey of the frontiers in finance’, The Economist, 9 October.

A series of excellent easy-to-read articles on the use of mathematics for predicting share prices.

Roberts, H.V. (1959) ‘Stock market ‘patterns” and financial analysis: Methodological suggestions’, Journal of Finance, March, pp. 1–10.

Describes chance-generated price series to cast doubt on technical analysis.

Roll, R. (1981) ‘A possible explanation for the small firm effect’, Journal of Finance, September.

Interesting consideration of the issue.

Roll, R. (1994) ‘What every CFO should know about scientific progress in financial economics: What is known and what remains to be resolved’, Financial Management, 23(2) (Summer), pp. 69–75.

A discussion, in straightforward terms, of Roll’s views on the state of play in the efficiency/inefficiency debate.

Rosenberg, B., Reid, K. and Lanstein, R. (1985) ‘Persuasive evidence of market inefficiency’, Journal of Portfolio Management, 11, Spring, pp. 9–16.

Reports the identification of two market inefficiencies.

Rouwenhorst, K.G. (1998) ‘International momentum strategies’, Journal of Finance, 53(1), February, pp. 267–84.

Price momentum evidence for 12 countries.

Rouwenhorst, K.G. (1999) ‘Local return factors and turnover in emerging stock markets’, Journal of Finance, 54(4), pp. 1439–63.

Emerging stock markets exhibit price momentum.

Sagi, J.S. and Seasholes, M.S. (2007) ‘Firm-specific attributes and the cross-section of momentum’, Journal of Financial Economics, 84, pp. 389–434.

A number of firm characteristics drive momentum profits.

Schoenburg, E. (1990) ‘Stock price prediction using neural networks’, Neurocomputing, 2, pp. 17–27.

Some evidence of predictability.

Scholes, M. (1972) ‘The market for securities: Substitution versus price pressure effects of information on share prices’, Journal of Business, April, pp. 179–211.

Evidence that the issue of more shares does not depress share prices.

Shefrin, H. (2000) Beyond Greed and Fear. Boston, MA: Harvard Business School Press.

An important book in the field of the application of behavioural finance to inefficiency in the markets.

Shiller, R.J. (1981) ‘Do stock prices move too much to be justified by subsequent charges in dividends?’, American Economic Review, 71, pp. 421–36.

The volatility of US shares is too large to be explained by the volatility of dividends. Taken to be evidence of overreaction and investors’ pursuit of fads and the herd.

Shiller, R.J. (2000) Irrational Exuberance. Princeton, NJ: Princeton University Press.

Behavioural finance applied to the bubble at the turn of the millennium.

Shivakumar, L. (2006) ‘Accruals, cash flows and the post-earnings-announcement drift’, Journal of Business Finance and Accounting, Jan–Mar, 33(1), pp. 1–25.

Earnings surprises cause post-earnings-announcement drift. However, if earnings are broken down into cash flow and accruals we find cash flows can predict future returns above and beyond that predicted by earnings alone.

Shleifer, A. (2000) Inefficient Markets: An Introduction to Behavioural Finance. Oxford: Oxford University Press.

A landmark presentation of the case for the impact of human (irrational) behaviour in financial markets.

Shon, J. and Zhou, P. (2010) ‘Do divergent opinions explain the value premium?’ The Journal of Investing, Summer, pp. 53–62.

More evidence on value shares outperforming growth shares, focusing on book-to-market ratio.

Smith, C. (1986) ‘Investment banking and the capital acquisition process’, Journal of Financial Economics, 15, pp. 3–29.

Lists numerous studies that report a decrease in the share price when a share issue is announced.

Smithers, A. (2009) Wall Street Revalued: Imperfect Markets and Inept Central Bankers. Chichester: John Wiley & Sons Ltd.

An expert on security valuation and market history provides profound insight into the working of markets, emphasising that markets are neither perfectly efficient nor absurd casinos.

Soros, G. (1987) The Alchemy of Finance. New York: John Wiley & Sons. (Reprinted in 1994 with a new preface and a new foreword.)

Provides insight into the investment approach of a highly successful investor.

Soros, G. (1995) Soros on Soros. New York: John Wiley & Sons. Financial theory and personal reminiscence interwoven.

Soros, G. (1998) The Crisis of Global Capitalism. New York: Public Affairs.

More on market irrationality.

Soros, G. (2009) The Crash of 2008 and What It Means. New York: PublicAffairs.

The most famous billionaire hedge fund manager explains his reflexivity theory and its impact on market behaviour. Clearly not a believer in EHM.


Sullivan, R., Timmermann, A. and White, H. (1999) ‘Data-snooping, technical trading rule performance, and the bootstrap’, Journal of Finance, 54(5), pp. 1647ff.

A demonstration of false inferences being drawn from data. Many technical trading rules that had been shown to ‘work’ in other academic studies are shown to be false when data snooping is eliminated.

‘Symposium on some anomalous evidence on capital market efficiency’ (1977). A special issue of the Journal of Financial Economics, 6, June.

Generally technical articles, but useful for those pursuing the subject in depth.

Thaler, R. (ed.) (1993) Advances in Behavioural Finance. New York: Russell Sage Foundation.

An important book in the growth of this developing discipline.

Thaler, R. H. (2005) Advances in Behavioural Finance Volume II. Princeton, NJ: Russell Sage Foundation.

An important collection of key papers in this young discipline.

Urry, M. (1996) ‘The $45bn man makes his pitch’, Financial Times, Weekend Money, 11/12 May, p. 1.

An article on Buffett.

Vayanos, D. and Woolley, P. (2011) ‘An institutional theory of momentum and reversal.’ London School of Economics Working Paper.

An attempt to provide a rational explanation for momentum and reversal based on flows between fund managers.

West, K.D. (1988) ‘Bubbles, fads and stock price volatility tests: A partial evaluation’, Journal of Finance, 43(3), pp. 639–56.

A summary and interpretation of some of the literature on share price volatility. Noise trading by naive investors is discussed.

Xiao, Y. and Arnold, G. (2008) ‘Testing Benjamin Graham’s Net Current Asset Value Strategy in London’. Journal of Investing, 17(4), Winter, pp. 11–19.

Those shares listed on the London Stock Exchange in the period 1981 to 2005 with a net current asset value to market capitalisation ratio greater than 1.5 display significantly positive market-adjusted returns (annualised return up to 19.7 per cent per year) over five holding years. (Net current asset value is total current assets minus all liabilities – long and short liabilities.)

Case study recommendations

Please see www.pearsoned.co.uk/arnold for case study synopses

� Options Granting. Authors: Phillip E. Pfeifer and Robert Jenkins (2007). Darden School of Business. Available on Harvard website.

� Global equity markets. The case of Royal Dutch and Shell. Authors: Kenneth A. Froot and André. F. Perold (1997). Harvard Business School.

� The Harmonized savings plan at BP Amoco. Author: Luis M. Viceira (2000). Harvard Business School.

� Beta Management Co. Author: Michael E. Edelson (1993): Harvard Business School.

1 Explain the three forms of market efficiency.

2 Does the EMH imply perfect forecasting ability?

3 What does ‘random walk’ mean?

4 Reshape plc has just announced an increase in profit of 50 per cent. The market was expecting profits to double. What will happen to Reshape’s share price?

5 Can the market be said to be inefficient because some shares give higher returns than others?

6 What use is inside information in the trading of shares?

7 Why is it important for directors and other managers to communicate to shareholders and potential share-holders as much information as possible about the firm?

8 What are the implications of the EMH for investors?

9 What are the implications of the EMH for managers?

10 What are allocative, operational and pricing efficiency?

11 What are ‘technical analysis’ and ‘fundamental analysis’?

Self-review questions


1 Arsenal plc, the quoted football and leisure group, wins the cup and therefore can anticipate greater revenues and profits. Before the win in the final the share price was £13.

a What will happen to the share price following the final whistle of the winning game?b Which of the following suggests the market is efficient? (Assume that the market as a whole does not move and

that the only news is the football match win.)i The share price rises slowly over a period of two weeks to reach £15. ii The share price jumps to £18 on the day of the win and then falls back to £15 one week later.iii The share price moves immediately to £15 and does not move further relative to the market.

2 If Marks & Spencer has a 1 for 1 scrip issue when its share price is 550p what would you expect to happen to its share price in theory (no other influences) and in practice?

3 (Examination level) ‘The paradox of the efficient market hypothesis is that large numbers of investors have to disbe-lieve the hypothesis in order to maintain efficiency.’ Write an essay explaining the EMH and explain this statement.

4 (Examination level) ‘Of course the market is not efficient. I know lots of people from technical analysts to profes-sional fundamental analysts who have made packets of money on the market.’ Describe the terms ‘technical’ and ‘fundamental analyst’. Explain how some individuals might generate a satisfactory return from stock market invest-ment even if it is efficient.

5 (Examination level) It could be said that insufficient attention has been paid to psychological factors when explaining stock efficiency anomalies. Outline the efficient stock market hypothesis (EMH) and describe some of the evidence which casts doubts on the semi-strong level of the efficient market hypothesis for which psychological explanations might be useful.

6 (Examination level) The efficient market hypothesis, if true, encourages managers to act in shareholder wealth enhancing ways. Discuss this.

7 If the efficient market hypothesis is true an investor might as well select shares by sticking a pin into the Financial Times. Explain why this is not quite true.

8 Arcadura plc has been planning a major rights issue to raise £300m. The market has fallen by 10 per cent in the past four days and the investment bank adviser suggests that Arcadura wait another three or four months before trying to sell these new shares. Given that the market is efficient, evaluate the investment banker’s suggestion.

9 Chartism and fundamental analysis are traditional methods used by stock market investors to make buy or sell deci-sions. Explain why modern finance theory has contributed to the growing popularity of share index funds which have a simple strategy of buying and holding a broadly based portfolio.

10 (Examination level) ‘The world’s well developed stock markets are efficient at pricing shares for most of the people most of the time.’ Comment on this statement and explain what is meant by stock market efficiency.

11 (Examination level) The following statements are extracts from the detailed minutes taken at a Board meeting of Advance plc. This company is discussing the possibility of a new flotation on the main listed market of the London stock market.

Mr Adams (Production Director): ‘I have been following the stock market for many years as a private investor. I put great value on patterns of past share prices for predicting future movements. At the moment my charts are telling me that the market is about to rise significantly and therefore we will get a higher price for our shares if we wait a few months. This will benefit our existing shareholders as the new shareholders will not get their shares artificially cheap.’

Questions and problems

Questions with an icon are also available for practice in MyFinanceLab with additional supporting resources.


Mr Cluff: ‘I too have been investing in shares for years and quite frankly have concluded that following charts is akin to voodoo magic, and what is more, working hard analysing companies is a waste of effort. The market cannot be predicted. I now put all my money into tracker funds and forget analysis. Delaying our flotation is pointless, the market might just as easily go down.’

Required

Consider the efficient stock markets theory and relate it to Mr Adams’ and Mr Cluff’s comments.

12 ‘A number of companies were put off flotation on the London Stock Exchange in 2011 because the market was too low.’ Explain the efficient market hypothesis and assess the logic of such postponements.

13 The chief geneticist at Adams Horticultural plc has discovered a method for raising the yield of commercial crops by 20 per cent. The managing director will make an announcement to the Stock Exchange in one week which will result in a sharp rise in the share price. Describe the level of inefficiency this represents. Is the geneticist legally free to try to make money on the share price issue by buying now?

14 Rapid Growth plc has recently changed the methods of accounting for depreciation, stock and research and develop-ment, all of which have the effect of improving the reported profit figures. Consider whether the share price will rise as a result of these actions.

15 A famous and well-respected economist announces in a Sunday newspaper that the growth phase of the economy is over and a recessionary trend has begun. He bases his evidence on the results of a dozen surveys which have been conducted and made public by various economic institutes over the past three months. Should you sell all your shares? Explain the logic behind your answer with reference to the efficient market hypothesis.

16 Explain why professional and highly paid fund managers generally produce returns less than those available on a broadly based market index.

17 (Examination level) Describe the extent to which the evidence supports the efficient market hypothesis.

Now retake your diagnostic test for Chapter 13 to check your progress and update your study plan.

Consider the actions of the directors of a stock-market quoted company you know well. Do they behave in such a way as to convince you they believe in the efficiency of the stock market? In what ways could they take steps to ensure greater efficiency of stock market pricing of the company’s shares?

Assignment

Visit www.pearsoned.co.uk/arnold to get access to Gradetracker diagnostic tests, podcasts, Excel spreadsheet solutions, FT articles, a flashcard revision tool, web links, a searchable glossary and more.

market efficiency hypothesis

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